Required Packages

library(foreign)
library(ggpubr)
library(MVN)
library(lavaan)
library(tidyverse)
library(semPlot)
library(car)
library(moments)
library(dplyr)
library(psych)

Reading the Dataset

MOREX = read.table("C:\\Users\\User\\Desktop\\R-PROJECT\\MEX_R.dat", header=TRUE)
View (MOREX)
head(MOREX)
##   NEG1 NEG2 NEG3 NEG5 NEGn DISC      MIN      MEX   SDO      RWA sdo1 sdo2
## 1    2    3    4    2    6  3.4 4.000000 4.000000 3.250 4.333333    1    2
## 2    1    1    1    1    1  1.0 6.333333 1.666667 1.000 1.333333    1    1
## 3    1    1    5    1    1  1.8 3.666667 4.333333 1.625 1.333333    1    1
## 4    3    3    3    3    4  3.2 4.333333 3.666667 2.500 2.166667    1    1
## 5    1    2    2    7    4  3.2 3.000000 5.000000 1.000 1.000000    1    1
## 6    6    7    6    1    4  4.8 2.666667 5.333333 1.625 2.666667    2    3
##   sdo3 sdo4 sdo5 sdo6 sdo7 sdo8 MEX1 MEX2 MEX3 rwa1 rwa2 rwa3 rwa4 rwa5
## 1    5    4    5    6    1    2    3    4    5    2    5    4    4    5
## 2    1    1    1    1    1    1    1    1    3    3    1    1    1    1
## 3    2    1    2    3    1    2    1    6    6    2    1    1    1    1
## 4    1    1    6    6    2    2    2    4    5    3    2    2    2    2
## 5    1    1    1    1    1    1    1    7    7    1    1    1    1    1
## 6    3    1    1    1    1    1    2    7    7    2    4    4    1    1
##   rwa6 age gender SDOI SDOII SDOIII SDOIV
## 1    6  60      2  1.5   4.5    5.5   1.5
## 2    1  69      2  1.0   1.0    1.0   1.0
## 3    2  51      2  1.0   1.5    2.5   1.5
## 4    2  66      2  1.0   1.0    6.0   2.0
## 5    1  65      2  1.0   1.0    1.0   1.0
## 6    4  42      2  2.5   2.0    1.0   1.0
nrow(MOREX)
## [1] 1015

Executive Summary

Exploratory data analyses were performed and the study’s hypotheses were tested by performing two stractural equation models. Exploratory data analyses contained checking for missing values, exploring the nature of the variables by summarizing the data, checking for the outliers, checking for normality of the variables, checking for the reliability of the scales, and performing correlation analyses. Correlational analyses were run between items of Discriminatory Intergroup Attitude Scale, Moral Exclusion Scale, Right-Wing Authoritarianism Scale, and Social Dominance Orientation Scale. By the first hypothesis we assumed that there will be a significant and positive association between Right-Wing Authoritarianism as well as Social Dominance Orientation and discriminatory attitudes against the Roma people. The hypothesis was supported, as it was found that both Right-Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO) significantly and positively predicted negative intergroup attitudes against the Roma people. The second hypothesis was that the aformentioned association will be fully explained by moral exclusion. Our Second hypothesis was partially supported and it was found that SDO and RWA partially mediated the effects.

Description of the Dataset

In an online survey study, 1015 Hungarian participants (\(M_{age}\) = 43.9, \(SD\) = 14.18; 523 female, 492 male) were recruited a set of 7-point likert type scales. The questionnaire measured Right-Wing Authoritarianism (RWA, hereafter), Social Dominance Orientation (SDO, hereafter), Moral Exclusion (MEX, hereafter), and five items measuring discriminatory attitudes against the Roma people residing in Hungary.

The dataset was originally in an SPSS file which was later converted to a dat file, and finally read by R.

The dataset belongs to an actual research which is aimed to be submitted as a manuscript to a scientific journal in the near future. Thus, it is supposed to be treated confidentially.

Exploratory Data Analyses

Exploring Missing Variables in the Dataset

sapply(MOREX,function(x) sum(is.na(x)))
##   NEG1   NEG2   NEG3   NEG5   NEGn   DISC    MIN    MEX    SDO    RWA 
##      0      0      0      0      0      0      0      0      0      0 
##   sdo1   sdo2   sdo3   sdo4   sdo5   sdo6   sdo7   sdo8   MEX1   MEX2 
##      0      0      0      0      0      0      0      0      0      0 
##   MEX3   rwa1   rwa2   rwa3   rwa4   rwa5   rwa6    age gender   SDOI 
##      0      0      0      0      0      0      0      0      0      0 
##  SDOII SDOIII  SDOIV 
##      0      0      0

Exploring the Nature of the Variables

MOREX%>%
  select(-gender, -age)%>%
  summary()
##       NEG1           NEG2            NEG3            NEG5      
##  Min.   :1.00   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.00   1st Qu.:1.000   1st Qu.:2.000   1st Qu.:1.000  
##  Median :2.00   Median :4.000   Median :4.000   Median :2.000  
##  Mean   :2.73   Mean   :3.828   Mean   :3.866   Mean   :2.903  
##  3rd Qu.:4.00   3rd Qu.:6.000   3rd Qu.:6.000   3rd Qu.:4.000  
##  Max.   :7.00   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##       NEGn            DISC           MIN             MEX       
##  Min.   :1.000   Min.   :1.00   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:2.00   1st Qu.:3.000   1st Qu.:3.000  
##  Median :2.000   Median :3.00   Median :4.000   Median :4.000  
##  Mean   :2.725   Mean   :3.21   Mean   :3.931   Mean   :4.069  
##  3rd Qu.:4.000   3rd Qu.:4.20   3rd Qu.:5.000   3rd Qu.:5.000  
##  Max.   :7.000   Max.   :7.00   Max.   :7.000   Max.   :7.000  
##       SDO             RWA             sdo1            sdo2      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.750   1st Qu.:1.667   1st Qu.:1.000   1st Qu.:1.000  
##  Median :2.750   Median :2.667   Median :2.000   Median :1.000  
##  Mean   :2.822   Mean   :2.799   Mean   :2.761   Mean   :2.243  
##  3rd Qu.:3.750   3rd Qu.:3.667   3rd Qu.:4.000   3rd Qu.:3.000  
##  Max.   :7.000   Max.   :6.833   Max.   :7.000   Max.   :7.000  
##       sdo3            sdo4            sdo5            sdo6      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:2.000  
##  Median :3.000   Median :1.000   Median :3.000   Median :4.000  
##  Mean   :3.062   Mean   :2.527   Mean   :3.369   Mean   :4.307  
##  3rd Qu.:5.000   3rd Qu.:4.000   3rd Qu.:5.000   3rd Qu.:7.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##       sdo7            sdo8            MEX1            MEX2      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:3.000  
##  Median :1.000   Median :2.000   Median :1.000   Median :5.000  
##  Mean   :1.865   Mean   :2.437   Mean   :2.229   Mean   :4.655  
##  3rd Qu.:2.000   3rd Qu.:4.000   3rd Qu.:3.000   3rd Qu.:7.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##       MEX3            rwa1            rwa2            rwa3      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:4.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :6.000   Median :2.000   Median :3.000   Median :2.000  
##  Mean   :5.322   Mean   :2.815   Mean   :3.448   Mean   :2.809  
##  3rd Qu.:7.000   3rd Qu.:4.000   3rd Qu.:6.000   3rd Qu.:4.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##       rwa4            rwa5            rwa6            SDOI      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :2.000   Median :2.000   Median :1.000   Median :2.000  
##  Mean   :2.556   Mean   :2.659   Mean   :2.507   Mean   :2.502  
##  3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.500  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000   Max.   :7.000  
##      SDOII           SDOIII          SDOIV      
##  Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:2.500   1st Qu.:1.000  
##  Median :2.500   Median :4.000   Median :1.500  
##  Mean   :2.795   Mean   :3.838   Mean   :2.151  
##  3rd Qu.:4.000   3rd Qu.:5.500   3rd Qu.:3.000  
##  Max.   :7.000   Max.   :7.000   Max.   :7.000
MOREX%>%
  select(-gender)%>%
  describe()
##        vars    n  mean    sd median trimmed   mad min   max range  skew
## NEG1      1 1015  2.73  2.02   2.00    2.43  1.48   1  7.00  6.00  0.88
## NEG2      2 1015  3.83  2.37   4.00    3.78  4.45   1  7.00  6.00  0.09
## NEG3      3 1015  3.87  2.04   4.00    3.83  2.97   1  7.00  6.00  0.11
## NEG5      4 1015  2.90  2.00   2.00    2.64  1.48   1  7.00  6.00  0.77
## NEGn      5 1015  2.73  1.83   2.00    2.45  1.48   1  7.00  6.00  0.87
## DISC      6 1015  3.21  1.53   3.00    3.12  1.78   1  7.00  6.00  0.42
## MIN       7 1015  3.93  1.56   4.00    3.90  1.48   1  7.00  6.00  0.18
## MEX       8 1015  4.07  1.56   4.00    4.10  1.48   1  7.00  6.00 -0.18
## SDO       9 1015  2.82  1.23   2.75    2.76  1.48   1  7.00  6.00  0.39
## RWA      10 1015  2.80  1.32   2.67    2.72  1.48   1  6.83  5.83  0.45
## sdo1     11 1015  2.76  1.90   2.00    2.51  1.48   1  7.00  6.00  0.71
## sdo2     12 1015  2.24  1.87   1.00    1.86  0.00   1  7.00  6.00  1.32
## sdo3     13 1015  3.06  2.09   3.00    2.83  2.97   1  7.00  6.00  0.56
## sdo4     14 1015  2.53  2.00   1.00    2.18  0.00   1  7.00  6.00  1.06
## sdo5     15 1015  3.37  2.16   3.00    3.21  2.97   1  7.00  6.00  0.37
## sdo6     16 1015  4.31  2.22   4.00    4.38  2.97   1  7.00  6.00 -0.22
## sdo7     17 1015  1.87  1.50   1.00    1.52  0.00   1  7.00  6.00  1.89
## sdo8     18 1015  2.44  1.76   2.00    2.14  1.48   1  7.00  6.00  1.09
## MEX1     19 1015  2.23  1.66   1.00    1.91  0.00   1  7.00  6.00  1.32
## MEX2     20 1015  4.66  2.01   5.00    4.81  2.97   1  7.00  6.00 -0.33
## MEX3     21 1015  5.32  1.89   6.00    5.60  1.48   1  7.00  6.00 -0.82
## rwa1     22 1015  2.81  1.82   2.00    2.59  1.48   1  7.00  6.00  0.66
## rwa2     23 1015  3.45  2.27   3.00    3.31  2.97   1  7.00  6.00  0.35
## rwa3     24 1015  2.81  1.88   2.00    2.57  1.48   1  7.00  6.00  0.73
## rwa4     25 1015  2.56  1.84   2.00    2.27  1.48   1  7.00  6.00  0.90
## rwa5     26 1015  2.66  1.96   2.00    2.37  1.48   1  7.00  6.00  0.87
## rwa6     27 1015  2.51  1.88   1.00    2.20  0.00   1  7.00  6.00  0.95
## age      28 1015 43.92 14.18  43.00   43.97 17.79  18 69.00 51.00 -0.01
## SDOI     29 1015  2.50  1.60   2.00    2.27  1.48   1  7.00  6.00  0.94
## SDOII    30 1015  2.79  1.76   2.50    2.58  2.22   1  7.00  6.00  0.69
## SDOIII   31 1015  3.84  1.90   4.00    3.80  2.22   1  7.00  6.00  0.02
## SDOIV    32 1015  2.15  1.48   1.50    1.89  0.74   1  7.00  6.00  1.39
##        kurtosis   se
## NEG1      -0.52 0.06
## NEG2      -1.55 0.07
## NEG3      -1.17 0.06
## NEG5      -0.65 0.06
## NEGn      -0.20 0.06
## DISC      -0.58 0.05
## MIN       -0.76 0.05
## MEX       -0.76 0.05
## SDO       -0.46 0.04
## RWA       -0.59 0.04
## sdo1      -0.68 0.06
## sdo2       0.45 0.06
## sdo3      -1.03 0.07
## sdo4      -0.21 0.06
## sdo5      -1.27 0.07
## sdo6      -1.36 0.07
## sdo7       2.89 0.05
## sdo8       0.17 0.06
## MEX1       0.90 0.05
## MEX2      -1.12 0.06
## MEX3      -0.48 0.06
## rwa1      -0.66 0.06
## rwa2      -1.38 0.07
## rwa3      -0.65 0.06
## rwa4      -0.31 0.06
## rwa5      -0.55 0.06
## rwa6      -0.37 0.06
## age       -1.16 0.45
## SDOI       0.08 0.05
## SDOII     -0.51 0.06
## SDOIII    -1.04 0.06
## SDOIV      1.37 0.05
table(MOREX$gender)
## 
##   1   2 
## 492 523
prop.table(table(MOREX$gender))
## 
##         1         2 
## 0.4847291 0.5152709

Reliability Analysis

tbl_df(MOREX)
## # A tibble: 1,015 x 33
##     NEG1  NEG2  NEG3  NEG5  NEGn  DISC   MIN   MEX   SDO   RWA  sdo1  sdo2
##    <int> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
##  1     2     3     4     2     6   3.4  4     4     3.25  4.33     1     2
##  2     1     1     1     1     1   1    6.33  1.67  1     1.33     1     1
##  3     1     1     5     1     1   1.8  3.67  4.33  1.62  1.33     1     1
##  4     3     3     3     3     4   3.2  4.33  3.67  2.5   2.17     1     1
##  5     1     2     2     7     4   3.2  3     5     1     1        1     1
##  6     6     7     6     1     4   4.8  2.67  5.33  1.62  2.67     2     3
##  7     1     1     1     1     1   1    7     1     1     1        1     1
##  8     1     1     6     2     4   2.8  7     1     1.38  2.17     1     1
##  9     1     1     2     6     5   3    4.67  3.33  2.12  1        2     1
## 10     1     1     1     1     1   1    7     1     1     1        1     1
## # ... with 1,005 more rows, and 21 more variables: sdo3 <int>, sdo4 <int>,
## #   sdo5 <int>, sdo6 <int>, sdo7 <int>, sdo8 <int>, MEX1 <int>,
## #   MEX2 <int>, MEX3 <int>, rwa1 <int>, rwa2 <int>, rwa3 <int>,
## #   rwa4 <int>, rwa5 <int>, rwa6 <int>, age <int>, gender <int>,
## #   SDOI <dbl>, SDOII <dbl>, SDOIII <dbl>, SDOIV <dbl>
SOCIALDO <- select(MOREX, 11, 12, 13, 14, 15, 16, 17, 18)
alpha(SOCIALDO)
## 
## Reliability analysis   
## Call: alpha(x = SOCIALDO)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean  sd median_r
##       0.78      0.79     0.8      0.31 3.7 0.01  2.8 1.2      0.3
## 
##  lower alpha upper     95% confidence boundaries
## 0.76 0.78 0.8 
## 
##  Reliability if an item is dropped:
##      raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## sdo1      0.75      0.75    0.77      0.30 3.1    0.012 0.0173  0.29
## sdo2      0.77      0.77    0.78      0.32 3.3    0.011 0.0183  0.33
## sdo3      0.75      0.75    0.76      0.30 3.0    0.012 0.0175  0.30
## sdo4      0.76      0.76    0.78      0.31 3.2    0.011 0.0176  0.30
## sdo5      0.75      0.76    0.77      0.31 3.2    0.012 0.0159  0.30
## sdo6      0.77      0.77    0.78      0.33 3.4    0.011 0.0129  0.30
## sdo7      0.78      0.78    0.77      0.33 3.5    0.011 0.0071  0.33
## sdo8      0.75      0.75    0.74      0.29 2.9    0.012 0.0135  0.29
## 
##  Item statistics 
##         n raw.r std.r r.cor r.drop mean  sd
## sdo1 1015  0.67  0.67  0.61   0.55  2.8 1.9
## sdo2 1015  0.59  0.60  0.50   0.45  2.2 1.9
## sdo3 1015  0.69  0.69  0.63   0.56  3.1 2.1
## sdo4 1015  0.63  0.63  0.55   0.48  2.5 2.0
## sdo5 1015  0.67  0.64  0.57   0.52  3.4 2.2
## sdo6 1015  0.61  0.57  0.49   0.43  4.3 2.2
## sdo7 1015  0.50  0.55  0.49   0.37  1.9 1.5
## sdo8 1015  0.68  0.71  0.69   0.56  2.4 1.8
## 
## Non missing response frequency for each item
##         1    2    3    4    5    6    7 miss
## sdo1 0.42 0.11 0.11 0.16 0.10 0.04 0.06    0
## sdo2 0.61 0.08 0.08 0.08 0.05 0.04 0.06    0
## sdo3 0.38 0.11 0.09 0.14 0.11 0.06 0.10    0
## sdo4 0.52 0.12 0.08 0.10 0.07 0.04 0.08    0
## sdo5 0.32 0.13 0.08 0.16 0.09 0.09 0.13    0
## sdo6 0.19 0.08 0.08 0.18 0.10 0.12 0.26    0
## sdo7 0.65 0.13 0.07 0.07 0.03 0.01 0.03    0
## sdo8 0.47 0.16 0.11 0.13 0.05 0.04 0.05    0
DISCRIMINATION <- select(MOREX, 1, 2, 3, 4, 5) 
alpha(DISCRIMINATION)
## 
## Reliability analysis   
## Call: alpha(x = DISCRIMINATION)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean  sd median_r
##        0.8       0.8    0.78      0.44   4 0.01  3.2 1.5     0.43
## 
##  lower alpha upper     95% confidence boundaries
## 0.78 0.8 0.82 
## 
##  Reliability if an item is dropped:
##      raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## NEG1      0.75      0.76    0.71      0.44 3.2    0.013 0.0073  0.43
## NEG2      0.76      0.77    0.72      0.45 3.3    0.012 0.0066  0.45
## NEG3      0.78      0.79    0.75      0.48 3.7    0.011 0.0056  0.45
## NEG5      0.74      0.74    0.70      0.42 2.9    0.013 0.0076  0.42
## NEGn      0.75      0.75    0.71      0.43 3.0    0.013 0.0081  0.44
## 
##  Item statistics 
##         n raw.r std.r r.cor r.drop mean  sd
## NEG1 1015  0.75  0.75  0.67   0.60  2.7 2.0
## NEG2 1015  0.76  0.73  0.64   0.57  3.8 2.4
## NEG3 1015  0.68  0.68  0.55   0.49  3.9 2.0
## NEG5 1015  0.78  0.79  0.72   0.64  2.9 2.0
## NEGn 1015  0.75  0.77  0.70   0.61  2.7 1.8
## 
## Non missing response frequency for each item
##         1    2    3    4    5    6    7 miss
## NEG1 0.44 0.13 0.09 0.14 0.06 0.05 0.09    0
## NEG2 0.30 0.08 0.07 0.13 0.09 0.09 0.23    0
## NEG3 0.19 0.11 0.14 0.21 0.10 0.10 0.16    0
## NEG5 0.36 0.18 0.11 0.13 0.07 0.05 0.09    0
## NEGn 0.37 0.18 0.11 0.20 0.04 0.03 0.07    0
RIGHTWA <- select(MOREX, 22, 23, 24, 25, 26, 27)
alpha(RIGHTWA)
## 
## Reliability analysis   
## Call: alpha(x = RIGHTWA)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean  sd median_r
##       0.76      0.76    0.77      0.35 3.2 0.012  2.8 1.3      0.3
## 
##  lower alpha upper     95% confidence boundaries
## 0.74 0.76 0.78 
## 
##  Reliability if an item is dropped:
##      raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## rwa1      0.73      0.73    0.73      0.35 2.7    0.013 0.020  0.31
## rwa2      0.72      0.72    0.69      0.34 2.5    0.014 0.010  0.31
## rwa3      0.71      0.72    0.69      0.34 2.6    0.014 0.010  0.31
## rwa4      0.73      0.73    0.73      0.35 2.7    0.013 0.021  0.31
## rwa5      0.70      0.70    0.70      0.32 2.4    0.015 0.020  0.28
## rwa6      0.75      0.75    0.76      0.38 3.1    0.012 0.022  0.33
## 
##  Item statistics 
##         n raw.r std.r r.cor r.drop mean  sd
## rwa1 1015  0.65  0.66  0.55   0.48  2.8 1.8
## rwa2 1015  0.73  0.70  0.66   0.54  3.4 2.3
## rwa3 1015  0.71  0.70  0.65   0.55  2.8 1.9
## rwa4 1015  0.65  0.66  0.55   0.48  2.6 1.8
## rwa5 1015  0.74  0.74  0.68   0.59  2.7 2.0
## rwa6 1015  0.59  0.59  0.44   0.39  2.5 1.9
## 
## Non missing response frequency for each item
##         1    2    3    4    5    6    7 miss
## rwa1 0.37 0.15 0.10 0.20 0.07 0.06 0.05    0
## rwa2 0.33 0.12 0.08 0.14 0.07 0.09 0.17    0
## rwa3 0.37 0.17 0.11 0.16 0.07 0.07 0.05    0
## rwa4 0.48 0.11 0.08 0.19 0.05 0.04 0.05    0
## rwa5 0.46 0.13 0.09 0.13 0.07 0.06 0.06    0
## rwa6 0.51 0.11 0.07 0.15 0.07 0.05 0.05    0
MORALEX <- select(MOREX, 19, 20, 21)
alpha(MORALEX)
## 
## Reliability analysis   
## Call: alpha(x = MORALEX)
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean  sd median_r
##       0.78      0.78    0.75      0.54 3.5 0.012  4.1 1.6     0.46
## 
##  lower alpha upper     95% confidence boundaries
## 0.76 0.78 0.81 
## 
##  Reliability if an item is dropped:
##      raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## MEX1      0.86      0.86    0.76      0.76 6.4   0.0085    NA  0.76
## MEX2      0.56      0.57    0.40      0.40 1.3   0.0272    NA  0.40
## MEX3      0.63      0.63    0.46      0.46 1.7   0.0230    NA  0.46
## 
##  Item statistics 
##         n raw.r std.r r.cor r.drop mean  sd
## MEX1 1015  0.72  0.74  0.50   0.46  2.2 1.7
## MEX2 1015  0.90  0.89  0.85   0.74  4.7 2.0
## MEX3 1015  0.87  0.86  0.81   0.69  5.3 1.9
## 
## Non missing response frequency for each item
##         1    2    3    4    5    6    7 miss
## MEX1 0.53 0.14 0.11 0.12 0.04 0.02 0.04    0
## MEX2 0.10 0.08 0.12 0.19 0.11 0.13 0.29    0
## MEX3 0.06 0.04 0.08 0.17 0.10 0.11 0.44    0

Check for outliers

Boxplots

MOREX %>% 
  gather(variable, value, -gender, -age) %>%
  ggplot() + 
  aes(x = fct_rev(variable), y = value) +
  geom_boxplot() +
  coord_flip()

Inquring into sdo2

OutlierS were prefered to be kept in the dataset.

outlierKD <- function(dt, var) {
     var_name <- eval(substitute(var),eval(dt))
     na1 <- sum(is.na(var_name))
     m1 <- mean(var_name, na.rm = T)
     par(mfrow=c(2, 2), oma=c(0,0,3,0))
     boxplot(var_name, main="With outliers")
     hist(var_name, main="With outliers", xlab=NA, ylab=NA)
     outlier <- boxplot.stats(var_name)$out
     mo <- mean(outlier)
     var_name <- ifelse(var_name %in% outlier, NA, var_name)
     boxplot(var_name, main="Without outliers")
     hist(var_name, main="Without outliers", xlab=NA, ylab=NA)
     title("Outlier Check", outer=TRUE)
     na2 <- sum(is.na(var_name))
     cat("Outliers identified:", na2 - na1, "n")
     cat("Propotion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name))*100, 1), "n")
     cat("Mean of the outliers:", round(mo, 2), "n")
     m2 <- mean(var_name, na.rm = T)
     cat("Mean without removing outliers:", round(m1, 2), "n")
     cat("Mean if we remove outliers:", round(m2, 2), "n")
     response <- readline(prompt="Do you want to remove outliers and to replace with NA? [yes/no]: ")
     if(response == "y" | response == "yes"){
          dt[as.character(substitute(var))] <- invisible(var_name)
          assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv)
          cat("Outliers successfully removed", "n")
          return(invisible(dt))
     } else{
          cat("Nothing changed", "n")
          return(invisible(var_name)) }}
outlierKD(MOREX, sdo2)

## Outliers identified: 58 nPropotion (%) of outliers: 6.1 nMean of the outliers: 7 nMean without removing outliers: 2.24 nMean if we remove outliers: 1.96 nDo you want to remove outliers and to replace with NA? [yes/no]: 
## Nothing changed n

Inquring into sdo7

OutlierS were prefered to be kept in the dataset.

outlierKD <- function(dt, var) {
     var_name <- eval(substitute(var),eval(dt))
     na1 <- sum(is.na(var_name))
     m1 <- mean(var_name, na.rm = T)
     par(mfrow=c(2, 2), oma=c(0,0,3,0))
     boxplot(var_name, main="With outliers")
     hist(var_name, main="With outliers", xlab=NA, ylab=NA)
     outlier <- boxplot.stats(var_name)$out
     mo <- mean(outlier)
     var_name <- ifelse(var_name %in% outlier, NA, var_name)
     boxplot(var_name, main="Without outliers")
     hist(var_name, main="Without outliers", xlab=NA, ylab=NA)
     title("Outlier Check", outer=TRUE)
     na2 <- sum(is.na(var_name))
     cat("Outliers identified:", na2 - na1, "n")
     cat("Propotion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name))*100, 1), "n")
     cat("Mean of the outliers:", round(mo, 2), "n")
     m2 <- mean(var_name, na.rm = T)
     cat("Mean without removing outliers:", round(m1, 2), "n")
     cat("Mean if we remove outliers:", round(m2, 2), "n")
     response <- readline(prompt="Do you want to remove outliers and to replace with NA? [yes/no]: ")
     if(response == "y" | response == "yes"){
          dt[as.character(substitute(var))] <- invisible(var_name)
          assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv)
          cat("Outliers successfully removed", "n")
          return(invisible(dt))
     } else{
          cat("Nothing changed", "n")
          return(invisible(var_name)) }}
outlierKD(MOREX, sdo7)

## Outliers identified: 152 nPropotion (%) of outliers: 17.6 nMean of the outliers: 5 nMean without removing outliers: 1.87 nMean if we remove outliers: 1.31 nDo you want to remove outliers and to replace with NA? [yes/no]: 
## Nothing changed n

Inquring into MEX1

OutlierS were prefered to be kept in the dataset.

outlierKD <- function(dt, var) {
     var_name <- eval(substitute(var),eval(dt))
     na1 <- sum(is.na(var_name))
     m1 <- mean(var_name, na.rm = T)
     par(mfrow=c(2, 2), oma=c(0,0,3,0))
     boxplot(var_name, main="With outliers")
     hist(var_name, main="With outliers", xlab=NA, ylab=NA)
     outlier <- boxplot.stats(var_name)$out
     mo <- mean(outlier)
     var_name <- ifelse(var_name %in% outlier, NA, var_name)
     boxplot(var_name, main="Without outliers")
     hist(var_name, main="Without outliers", xlab=NA, ylab=NA)
     title("Outlier Check", outer=TRUE)
     na2 <- sum(is.na(var_name))
     cat("Outliers identified:", na2 - na1, "n")
     cat("Propotion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name))*100, 1), "n")
     cat("Mean of the outliers:", round(mo, 2), "n")
     m2 <- mean(var_name, na.rm = T)
     cat("Mean without removing outliers:", round(m1, 2), "n")
     cat("Mean if we remove outliers:", round(m2, 2), "n")
     response <- readline(prompt="Do you want to remove outliers and to replace with NA? [yes/no]: ")
     if(response == "y" | response == "yes"){
          dt[as.character(substitute(var))] <- invisible(var_name)
          assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv)
          cat("Outliers successfully removed", "n")
          return(invisible(dt))
     } else{
          cat("Nothing changed", "n")
          return(invisible(var_name)) }}
outlierKD(MOREX, MEX1)

## Outliers identified: 40 nPropotion (%) of outliers: 4.1 nMean of the outliers: 7 nMean without removing outliers: 2.23 nMean if we remove outliers: 2.03 nDo you want to remove outliers and to replace with NA? [yes/no]: 
## Nothing changed n

Tests of Normality

None of the varibles were found to meet normal distribution assumtion. Since multivariate normality assumption is violated , it was decided to emply MLR estimator (\(maximum likelihood with robust corrections to standard errors\)) in the structural equation models.

MOREX %>% 
  gather(variable, value) %>%
  ggplot() + 
  aes(x = value) +
  geom_freqpoly() +
  facet_wrap(~variable)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

shapiro.test(MOREX$DISC)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$DISC
## W = 0.96166, p-value = 1.122e-15
skewness(MOREX$DISC)
## [1] 0.4223075
kurtosis(MOREX$DISC)
## [1] 2.427429
ggqqplot(MOREX$DISC)

hist(MOREX$DISC)

ggdensity(MOREX$DISC)

shapiro.test(MOREX$MEX)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$MEX
## W = 0.97359, p-value = 1.246e-12
skewness(MOREX$MEX)
## [1] -0.1765252
kurtosis(MOREX$MEX)
## [1] 2.240145
ggqqplot(MOREX$MEX)

hist(MOREX$MEX)

ggdensity(MOREX$MEX)

shapiro.test(MOREX$SDO)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$SDO
## W = 0.96801, p-value = 3.761e-14
skewness(MOREX$SDO)
## [1] 0.3884446
kurtosis(MOREX$SDO)
## [1] 2.542652
ggqqplot(MOREX$SDO)

hist(MOREX$SDO)

ggdensity(MOREX$SDO)

shapiro.test(MOREX$RWA)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$RWA
## W = 0.95527, p-value < 2.2e-16
skewness(MOREX$RWA)
## [1] 0.448127
kurtosis(MOREX$RWA)
## [1] 2.414649
ggqqplot(MOREX$RWA)

hist(MOREX$RWA)

ggdensity(MOREX$RWA)

shapiro.test(MOREX$MEX1)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$MEX1
## W = 0.75259, p-value < 2.2e-16
skewness(MOREX$MEX1)
## [1] 1.323717
kurtosis(MOREX$MEX1)
## [1] 3.910982
ggqqplot(MOREX$MEX1)

hist(MOREX$MEX1)

ggdensity(MOREX$MEX1)

shapiro.test(MOREX$MEX2)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$MEX2
## W = 0.88968, p-value < 2.2e-16
skewness(MOREX$MEX2)
## [1] -0.3320415
kurtosis(MOREX$MEX2)
## [1] 1.887003
ggqqplot(MOREX$MEX2)

hist(MOREX$MEX2)

ggdensity(MOREX$MEX2)

shapiro.test(MOREX$MEX3)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$MEX3
## W = 0.81768, p-value < 2.2e-16
skewness(MOREX$MEX3)
## [1] -0.8230591
kurtosis(MOREX$MEX3)
## [1] 2.526161
ggqqplot(MOREX$MEX3)

hist(MOREX$MEX3)

ggdensity(MOREX$MEX3)

shapiro.test(MOREX$NEG1)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$NEG1
## W = 0.80136, p-value < 2.2e-16
skewness(MOREX$NEG1)
## [1] 0.8830489
kurtosis(MOREX$NEG1)
## [1] 2.486659
ggqqplot(MOREX$NEG1)

hist(MOREX$NEG1)

ggdensity(MOREX$NEG1)

shapiro.test(MOREX$NEG2)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$NEG2
## W = 0.84597, p-value < 2.2e-16
skewness(MOREX$NEG2)
## [1] 0.08784638
kurtosis(MOREX$NEG2)
## [1] 1.454401
ggqqplot(MOREX$NEG2)

hist(MOREX$NEG2)

ggdensity(MOREX$NEG2)

shapiro.test(MOREX$NEG3)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$NEG3
## W = 0.90961, p-value < 2.2e-16
skewness(MOREX$NEG3)
## [1] 0.1077017
kurtosis(MOREX$NEG3)
## [1] 1.830878
ggqqplot(MOREX$NEG3)

hist(MOREX$NEG3)

ggdensity(MOREX$NEG3)

shapiro.test(MOREX$NEG5)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$NEG5
## W = 0.83852, p-value < 2.2e-16
skewness(MOREX$NEG5)
## [1] 0.7738774
kurtosis(MOREX$NEG5)
## [1] 2.355297
ggqqplot(MOREX$NEG5)

hist(MOREX$NEG5)

ggdensity(MOREX$NEG5)

shapiro.test(MOREX$NEGn)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$NEGn
## W = 0.83747, p-value < 2.2e-16
skewness(MOREX$NEGn)
## [1] 0.8748391
kurtosis(MOREX$NEGn)
## [1] 2.803275
ggqqplot(MOREX$NEGn)

hist(MOREX$NEGn)

ggdensity(MOREX$NEGn)

shapiro.test(MOREX$sdo1)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$sdo1
## W = 0.83322, p-value < 2.2e-16
skewness(MOREX$sdo1)
## [1] 0.7127712
kurtosis(MOREX$sdo1)
## [1] 2.321097
ggqqplot(MOREX$sdo1)

hist(MOREX$sdo1)

ggdensity(MOREX$sdo1)

shapiro.test(MOREX$sdo2)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$sdo2
## W = 0.69787, p-value < 2.2e-16
skewness(MOREX$sdo2)
## [1] 1.321446
kurtosis(MOREX$sdo2)
## [1] 3.453847
ggqqplot(MOREX$sdo2)

hist(MOREX$sdo2)

ggdensity(MOREX$sdo2)

shapiro.test(MOREX$sdo3)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$sdo3
## W = 0.84402, p-value < 2.2e-16
skewness(MOREX$sdo3)
## [1] 0.5605391
kurtosis(MOREX$sdo3)
## [1] 1.969982
ggqqplot(MOREX$sdo3)

hist(MOREX$sdo3)

ggdensity(MOREX$sdo3)

shapiro.test(MOREX$sdo4)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$sdo4
## W = 0.7569, p-value < 2.2e-16
skewness(MOREX$sdo4)
## [1] 1.061738
kurtosis(MOREX$sdo4)
## [1] 2.7928
ggqqplot(MOREX$sdo4)

hist(MOREX$sdo4)

ggdensity(MOREX$sdo4)

shapiro.test(MOREX$sdo5)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$sdo5
## W = 0.86441, p-value < 2.2e-16
skewness(MOREX$sdo5)
## [1] 0.3695372
kurtosis(MOREX$sdo5)
## [1] 1.734078
ggqqplot(MOREX$sdo5)

hist(MOREX$sdo5)

ggdensity(MOREX$sdo5)

shapiro.test(MOREX$sdo6)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$sdo6
## W = 0.87322, p-value < 2.2e-16
skewness(MOREX$sdo6)
## [1] -0.2187444
kurtosis(MOREX$sdo6)
## [1] 1.643743
ggqqplot(MOREX$sdo6)

hist(MOREX$sdo6)

ggdensity(MOREX$sdo6)

shapiro.test(MOREX$sdo7)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$sdo7
## W = 0.63873, p-value < 2.2e-16
skewness(MOREX$sdo7)
## [1] 1.89676
kurtosis(MOREX$sdo7)
## [1] 5.896715
ggqqplot(MOREX$sdo7)

hist(MOREX$sdo7)

ggdensity(MOREX$sdo7)

shapiro.test(MOREX$sdo8)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$sdo8
## W = 0.79258, p-value < 2.2e-16
skewness(MOREX$sdo8)
## [1] 1.091213
kurtosis(MOREX$sdo8)
## [1] 3.172406
ggqqplot(MOREX$sdo8)

hist(MOREX$sdo8)

ggdensity(MOREX$sdo8)

shapiro.test(MOREX$rwa1)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$rwa1
## W = 0.85753, p-value < 2.2e-16
skewness(MOREX$rwa1)
## [1] 0.661101
kurtosis(MOREX$rwa1)
## [1] 2.348878
ggqqplot(MOREX$rwa1)

hist(MOREX$rwa1)

ggdensity(MOREX$rwa1)

shapiro.test(MOREX$rwa2)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$rwa2
## W = 0.84719, p-value < 2.2e-16
skewness(MOREX$rwa2)
## [1] 0.350081
kurtosis(MOREX$rwa2)
## [1] 1.626343
ggqqplot(MOREX$rwa2)

hist(MOREX$rwa2)

ggdensity(MOREX$rwa2)

shapiro.test(MOREX$rwa3)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$rwa3
## W = 0.84745, p-value < 2.2e-16
skewness(MOREX$rwa3)
## [1] 0.7333391
kurtosis(MOREX$rwa3)
## [1] 2.35392
ggqqplot(MOREX$rwa3)

hist(MOREX$rwa3)

ggdensity(MOREX$rwa3)

shapiro.test(MOREX$rwa4)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$rwa4
## W = 0.79787, p-value < 2.2e-16
skewness(MOREX$rwa4)
## [1] 0.9014101
kurtosis(MOREX$rwa4)
## [1] 2.695604
ggqqplot(MOREX$rwa4)

hist(MOREX$rwa4)

ggdensity(MOREX$rwa4)

shapiro.test(MOREX$rwa5)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$rwa5
## W = 0.80114, p-value < 2.2e-16
skewness(MOREX$rwa5)
## [1] 0.8683063
kurtosis(MOREX$rwa5)
## [1] 2.453487
ggqqplot(MOREX$rwa5)

hist(MOREX$rwa5)

ggdensity(MOREX$rwa5)

shapiro.test(MOREX$rwa6)
## 
##  Shapiro-Wilk normality test
## 
## data:  MOREX$rwa6
## W = 0.77901, p-value < 2.2e-16
skewness(MOREX$rwa6)
## [1] 0.9471383
kurtosis(MOREX$rwa6)
## [1] 2.636909
ggqqplot(MOREX$rwa6)

hist(MOREX$rwa6)

ggdensity(MOREX$rwa6)

Some Multivariate Normality Tests

MOREX%>%
  select(-gender, -age)%>%
  mvn(mvnTest = "royston")
## $multivariateNormality
##      Test        H p value MVN
## 1 Royston 2387.091       0  NO
## 
## $univariateNormality
##            Test  Variable Statistic   p value Normality
## 1  Shapiro-Wilk   NEG1       0.8014  <0.001      NO    
## 2  Shapiro-Wilk   NEG2       0.8460  <0.001      NO    
## 3  Shapiro-Wilk   NEG3       0.9096  <0.001      NO    
## 4  Shapiro-Wilk   NEG5       0.8385  <0.001      NO    
## 5  Shapiro-Wilk   NEGn       0.8375  <0.001      NO    
## 6  Shapiro-Wilk   DISC       0.9617  <0.001      NO    
## 7  Shapiro-Wilk    MIN       0.9736  <0.001      NO    
## 8  Shapiro-Wilk    MEX       0.9736  <0.001      NO    
## 9  Shapiro-Wilk    SDO       0.9680  <0.001      NO    
## 10 Shapiro-Wilk    RWA       0.9553  <0.001      NO    
## 11 Shapiro-Wilk   sdo1       0.8332  <0.001      NO    
## 12 Shapiro-Wilk   sdo2       0.6979  <0.001      NO    
## 13 Shapiro-Wilk   sdo3       0.8440  <0.001      NO    
## 14 Shapiro-Wilk   sdo4       0.7569  <0.001      NO    
## 15 Shapiro-Wilk   sdo5       0.8644  <0.001      NO    
## 16 Shapiro-Wilk   sdo6       0.8732  <0.001      NO    
## 17 Shapiro-Wilk   sdo7       0.6387  <0.001      NO    
## 18 Shapiro-Wilk   sdo8       0.7926  <0.001      NO    
## 19 Shapiro-Wilk   MEX1       0.7526  <0.001      NO    
## 20 Shapiro-Wilk   MEX2       0.8897  <0.001      NO    
## 21 Shapiro-Wilk   MEX3       0.8177  <0.001      NO    
## 22 Shapiro-Wilk   rwa1       0.8575  <0.001      NO    
## 23 Shapiro-Wilk   rwa2       0.8472  <0.001      NO    
## 24 Shapiro-Wilk   rwa3       0.8475  <0.001      NO    
## 25 Shapiro-Wilk   rwa4       0.7979  <0.001      NO    
## 26 Shapiro-Wilk   rwa5       0.8011  <0.001      NO    
## 27 Shapiro-Wilk   rwa6       0.7790  <0.001      NO    
## 28 Shapiro-Wilk   SDOI       0.8551  <0.001      NO    
## 29 Shapiro-Wilk   SDOII      0.8765  <0.001      NO    
## 30 Shapiro-Wilk  SDOIII      0.9368  <0.001      NO    
## 31 Shapiro-Wilk   SDOIV      0.7833  <0.001      NO    
## 
## $Descriptives
##           n     Mean  Std.Dev   Median Min      Max     25th     75th
## NEG1   1015 2.730049 2.018170 2.000000   1 7.000000 1.000000 4.000000
## NEG2   1015 3.827586 2.369020 4.000000   1 7.000000 1.000000 6.000000
## NEG3   1015 3.866010 2.039979 4.000000   1 7.000000 2.000000 6.000000
## NEG5   1015 2.903448 2.000626 2.000000   1 7.000000 1.000000 4.000000
## NEGn   1015 2.725123 1.829063 2.000000   1 7.000000 1.000000 4.000000
## DISC   1015 3.210443 1.527786 3.000000   1 7.000000 2.000000 4.200000
## MIN    1015 3.931363 1.556165 4.000000   1 7.000000 3.000000 5.000000
## MEX    1015 4.068637 1.556165 4.000000   1 7.000000 3.000000 5.000000
## SDO    1015 2.821552 1.230008 2.750000   1 7.000000 1.750000 3.750000
## RWA    1015 2.799015 1.316253 2.666667   1 6.833333 1.666667 3.666667
## sdo1   1015 2.760591 1.899035 2.000000   1 7.000000 1.000000 4.000000
## sdo2   1015 2.243350 1.873434 1.000000   1 7.000000 1.000000 3.000000
## sdo3   1015 3.062069 2.090433 3.000000   1 7.000000 1.000000 5.000000
## sdo4   1015 2.527094 1.996794 1.000000   1 7.000000 1.000000 4.000000
## sdo5   1015 3.369458 2.163543 3.000000   1 7.000000 1.000000 5.000000
## sdo6   1015 4.307389 2.221931 4.000000   1 7.000000 2.000000 7.000000
## sdo7   1015 1.865025 1.501646 1.000000   1 7.000000 1.000000 2.000000
## sdo8   1015 2.437438 1.763586 2.000000   1 7.000000 1.000000 4.000000
## MEX1   1015 2.228571 1.662017 1.000000   1 7.000000 1.000000 3.000000
## MEX2   1015 4.655172 2.012611 5.000000   1 7.000000 3.000000 7.000000
## MEX3   1015 5.322167 1.894571 6.000000   1 7.000000 4.000000 7.000000
## rwa1   1015 2.814778 1.823358 2.000000   1 7.000000 1.000000 4.000000
## rwa2   1015 3.448276 2.273472 3.000000   1 7.000000 1.000000 6.000000
## rwa3   1015 2.808867 1.883417 2.000000   1 7.000000 1.000000 4.000000
## rwa4   1015 2.555665 1.842573 2.000000   1 7.000000 1.000000 4.000000
## rwa5   1015 2.659113 1.956644 2.000000   1 7.000000 1.000000 4.000000
## rwa6   1015 2.507389 1.882048 1.000000   1 7.000000 1.000000 4.000000
## SDOI   1015 2.501970 1.603742 2.000000   1 7.000000 1.000000 3.500000
## SDOII  1015 2.794581 1.761807 2.500000   1 7.000000 1.000000 4.000000
## SDOIII 1015 3.838424 1.900509 4.000000   1 7.000000 2.500000 5.500000
## SDOIV  1015 2.151232 1.476989 1.500000   1 7.000000 1.000000 3.000000
##               Skew    Kurtosis
## NEG1    0.88174425 -0.51823794
## NEG2    0.08771659 -1.54846305
## NEG3    0.10754259 -1.17272762
## NEG5    0.77273398 -0.64934212
## NEGn    0.87354659 -0.20224616
## DISC    0.42168352 -0.57735149
## MIN     0.17626442 -0.76426732
## MEX    -0.17626442 -0.76426732
## SDO     0.38787069 -0.46235602
## RWA     0.44746486 -0.59010696
## sdo1    0.71171812 -0.68347406
## sdo2    1.31949395  0.44704465
## sdo3    0.55971096 -1.03389821
## sdo4    1.06016930 -0.21270003
## sdo5    0.36899118 -1.26933685
## sdo6   -0.21842121 -1.35949408
## sdo7    1.89395727  2.88510149
## sdo8    1.08960095  0.16615795
## MEX1    1.32176151  0.90327954
## MEX2   -0.33155094 -1.11671341
## MEX3   -0.82184307 -0.47881378
## rwa1    0.66012423 -0.65574790
## rwa2    0.34956373 -1.37686027
## rwa3    0.73225563 -0.65071591
## rwa4    0.90007829 -0.30970443
## rwa5    0.86702345 -0.55134503
## rwa6    0.94573891 -0.36828477
## SDOI    0.93727862  0.07714219
## SDOII   0.68754350 -0.51024139
## SDOIII  0.01756050 -1.04435488
## SDOIV   1.38799904  1.37152716
MOREX%>%
  select(-gender, -age)%>%
  mvn(mvnTest = "hz")
## $multivariateNormality
##            Test   HZ p value MVN
## 1 Henze-Zirkler 4060       0  NO
## 
## $univariateNormality
##            Test  Variable Statistic   p value Normality
## 1  Shapiro-Wilk   NEG1       0.8014  <0.001      NO    
## 2  Shapiro-Wilk   NEG2       0.8460  <0.001      NO    
## 3  Shapiro-Wilk   NEG3       0.9096  <0.001      NO    
## 4  Shapiro-Wilk   NEG5       0.8385  <0.001      NO    
## 5  Shapiro-Wilk   NEGn       0.8375  <0.001      NO    
## 6  Shapiro-Wilk   DISC       0.9617  <0.001      NO    
## 7  Shapiro-Wilk    MIN       0.9736  <0.001      NO    
## 8  Shapiro-Wilk    MEX       0.9736  <0.001      NO    
## 9  Shapiro-Wilk    SDO       0.9680  <0.001      NO    
## 10 Shapiro-Wilk    RWA       0.9553  <0.001      NO    
## 11 Shapiro-Wilk   sdo1       0.8332  <0.001      NO    
## 12 Shapiro-Wilk   sdo2       0.6979  <0.001      NO    
## 13 Shapiro-Wilk   sdo3       0.8440  <0.001      NO    
## 14 Shapiro-Wilk   sdo4       0.7569  <0.001      NO    
## 15 Shapiro-Wilk   sdo5       0.8644  <0.001      NO    
## 16 Shapiro-Wilk   sdo6       0.8732  <0.001      NO    
## 17 Shapiro-Wilk   sdo7       0.6387  <0.001      NO    
## 18 Shapiro-Wilk   sdo8       0.7926  <0.001      NO    
## 19 Shapiro-Wilk   MEX1       0.7526  <0.001      NO    
## 20 Shapiro-Wilk   MEX2       0.8897  <0.001      NO    
## 21 Shapiro-Wilk   MEX3       0.8177  <0.001      NO    
## 22 Shapiro-Wilk   rwa1       0.8575  <0.001      NO    
## 23 Shapiro-Wilk   rwa2       0.8472  <0.001      NO    
## 24 Shapiro-Wilk   rwa3       0.8475  <0.001      NO    
## 25 Shapiro-Wilk   rwa4       0.7979  <0.001      NO    
## 26 Shapiro-Wilk   rwa5       0.8011  <0.001      NO    
## 27 Shapiro-Wilk   rwa6       0.7790  <0.001      NO    
## 28 Shapiro-Wilk   SDOI       0.8551  <0.001      NO    
## 29 Shapiro-Wilk   SDOII      0.8765  <0.001      NO    
## 30 Shapiro-Wilk  SDOIII      0.9368  <0.001      NO    
## 31 Shapiro-Wilk   SDOIV      0.7833  <0.001      NO    
## 
## $Descriptives
##           n     Mean  Std.Dev   Median Min      Max     25th     75th
## NEG1   1015 2.730049 2.018170 2.000000   1 7.000000 1.000000 4.000000
## NEG2   1015 3.827586 2.369020 4.000000   1 7.000000 1.000000 6.000000
## NEG3   1015 3.866010 2.039979 4.000000   1 7.000000 2.000000 6.000000
## NEG5   1015 2.903448 2.000626 2.000000   1 7.000000 1.000000 4.000000
## NEGn   1015 2.725123 1.829063 2.000000   1 7.000000 1.000000 4.000000
## DISC   1015 3.210443 1.527786 3.000000   1 7.000000 2.000000 4.200000
## MIN    1015 3.931363 1.556165 4.000000   1 7.000000 3.000000 5.000000
## MEX    1015 4.068637 1.556165 4.000000   1 7.000000 3.000000 5.000000
## SDO    1015 2.821552 1.230008 2.750000   1 7.000000 1.750000 3.750000
## RWA    1015 2.799015 1.316253 2.666667   1 6.833333 1.666667 3.666667
## sdo1   1015 2.760591 1.899035 2.000000   1 7.000000 1.000000 4.000000
## sdo2   1015 2.243350 1.873434 1.000000   1 7.000000 1.000000 3.000000
## sdo3   1015 3.062069 2.090433 3.000000   1 7.000000 1.000000 5.000000
## sdo4   1015 2.527094 1.996794 1.000000   1 7.000000 1.000000 4.000000
## sdo5   1015 3.369458 2.163543 3.000000   1 7.000000 1.000000 5.000000
## sdo6   1015 4.307389 2.221931 4.000000   1 7.000000 2.000000 7.000000
## sdo7   1015 1.865025 1.501646 1.000000   1 7.000000 1.000000 2.000000
## sdo8   1015 2.437438 1.763586 2.000000   1 7.000000 1.000000 4.000000
## MEX1   1015 2.228571 1.662017 1.000000   1 7.000000 1.000000 3.000000
## MEX2   1015 4.655172 2.012611 5.000000   1 7.000000 3.000000 7.000000
## MEX3   1015 5.322167 1.894571 6.000000   1 7.000000 4.000000 7.000000
## rwa1   1015 2.814778 1.823358 2.000000   1 7.000000 1.000000 4.000000
## rwa2   1015 3.448276 2.273472 3.000000   1 7.000000 1.000000 6.000000
## rwa3   1015 2.808867 1.883417 2.000000   1 7.000000 1.000000 4.000000
## rwa4   1015 2.555665 1.842573 2.000000   1 7.000000 1.000000 4.000000
## rwa5   1015 2.659113 1.956644 2.000000   1 7.000000 1.000000 4.000000
## rwa6   1015 2.507389 1.882048 1.000000   1 7.000000 1.000000 4.000000
## SDOI   1015 2.501970 1.603742 2.000000   1 7.000000 1.000000 3.500000
## SDOII  1015 2.794581 1.761807 2.500000   1 7.000000 1.000000 4.000000
## SDOIII 1015 3.838424 1.900509 4.000000   1 7.000000 2.500000 5.500000
## SDOIV  1015 2.151232 1.476989 1.500000   1 7.000000 1.000000 3.000000
##               Skew    Kurtosis
## NEG1    0.88174425 -0.51823794
## NEG2    0.08771659 -1.54846305
## NEG3    0.10754259 -1.17272762
## NEG5    0.77273398 -0.64934212
## NEGn    0.87354659 -0.20224616
## DISC    0.42168352 -0.57735149
## MIN     0.17626442 -0.76426732
## MEX    -0.17626442 -0.76426732
## SDO     0.38787069 -0.46235602
## RWA     0.44746486 -0.59010696
## sdo1    0.71171812 -0.68347406
## sdo2    1.31949395  0.44704465
## sdo3    0.55971096 -1.03389821
## sdo4    1.06016930 -0.21270003
## sdo5    0.36899118 -1.26933685
## sdo6   -0.21842121 -1.35949408
## sdo7    1.89395727  2.88510149
## sdo8    1.08960095  0.16615795
## MEX1    1.32176151  0.90327954
## MEX2   -0.33155094 -1.11671341
## MEX3   -0.82184307 -0.47881378
## rwa1    0.66012423 -0.65574790
## rwa2    0.34956373 -1.37686027
## rwa3    0.73225563 -0.65071591
## rwa4    0.90007829 -0.30970443
## rwa5    0.86702345 -0.55134503
## rwa6    0.94573891 -0.36828477
## SDOI    0.93727862  0.07714219
## SDOII   0.68754350 -0.51024139
## SDOIII  0.01756050 -1.04435488
## SDOIV   1.38799904  1.37152716
MOREX%>%
  select(-gender, -age)%>%
  mvn(mvnTest = "energy")
## Warning in sqrt(lambda): NaNs produced
## $multivariateNormality
##          Test Statistic p value MVN
## 1 E-statistic        NA      NA  NA
## 
## $univariateNormality
##            Test  Variable Statistic   p value Normality
## 1  Shapiro-Wilk   NEG1       0.8014  <0.001      NO    
## 2  Shapiro-Wilk   NEG2       0.8460  <0.001      NO    
## 3  Shapiro-Wilk   NEG3       0.9096  <0.001      NO    
## 4  Shapiro-Wilk   NEG5       0.8385  <0.001      NO    
## 5  Shapiro-Wilk   NEGn       0.8375  <0.001      NO    
## 6  Shapiro-Wilk   DISC       0.9617  <0.001      NO    
## 7  Shapiro-Wilk    MIN       0.9736  <0.001      NO    
## 8  Shapiro-Wilk    MEX       0.9736  <0.001      NO    
## 9  Shapiro-Wilk    SDO       0.9680  <0.001      NO    
## 10 Shapiro-Wilk    RWA       0.9553  <0.001      NO    
## 11 Shapiro-Wilk   sdo1       0.8332  <0.001      NO    
## 12 Shapiro-Wilk   sdo2       0.6979  <0.001      NO    
## 13 Shapiro-Wilk   sdo3       0.8440  <0.001      NO    
## 14 Shapiro-Wilk   sdo4       0.7569  <0.001      NO    
## 15 Shapiro-Wilk   sdo5       0.8644  <0.001      NO    
## 16 Shapiro-Wilk   sdo6       0.8732  <0.001      NO    
## 17 Shapiro-Wilk   sdo7       0.6387  <0.001      NO    
## 18 Shapiro-Wilk   sdo8       0.7926  <0.001      NO    
## 19 Shapiro-Wilk   MEX1       0.7526  <0.001      NO    
## 20 Shapiro-Wilk   MEX2       0.8897  <0.001      NO    
## 21 Shapiro-Wilk   MEX3       0.8177  <0.001      NO    
## 22 Shapiro-Wilk   rwa1       0.8575  <0.001      NO    
## 23 Shapiro-Wilk   rwa2       0.8472  <0.001      NO    
## 24 Shapiro-Wilk   rwa3       0.8475  <0.001      NO    
## 25 Shapiro-Wilk   rwa4       0.7979  <0.001      NO    
## 26 Shapiro-Wilk   rwa5       0.8011  <0.001      NO    
## 27 Shapiro-Wilk   rwa6       0.7790  <0.001      NO    
## 28 Shapiro-Wilk   SDOI       0.8551  <0.001      NO    
## 29 Shapiro-Wilk   SDOII      0.8765  <0.001      NO    
## 30 Shapiro-Wilk  SDOIII      0.9368  <0.001      NO    
## 31 Shapiro-Wilk   SDOIV      0.7833  <0.001      NO    
## 
## $Descriptives
##           n     Mean  Std.Dev   Median Min      Max     25th     75th
## NEG1   1015 2.730049 2.018170 2.000000   1 7.000000 1.000000 4.000000
## NEG2   1015 3.827586 2.369020 4.000000   1 7.000000 1.000000 6.000000
## NEG3   1015 3.866010 2.039979 4.000000   1 7.000000 2.000000 6.000000
## NEG5   1015 2.903448 2.000626 2.000000   1 7.000000 1.000000 4.000000
## NEGn   1015 2.725123 1.829063 2.000000   1 7.000000 1.000000 4.000000
## DISC   1015 3.210443 1.527786 3.000000   1 7.000000 2.000000 4.200000
## MIN    1015 3.931363 1.556165 4.000000   1 7.000000 3.000000 5.000000
## MEX    1015 4.068637 1.556165 4.000000   1 7.000000 3.000000 5.000000
## SDO    1015 2.821552 1.230008 2.750000   1 7.000000 1.750000 3.750000
## RWA    1015 2.799015 1.316253 2.666667   1 6.833333 1.666667 3.666667
## sdo1   1015 2.760591 1.899035 2.000000   1 7.000000 1.000000 4.000000
## sdo2   1015 2.243350 1.873434 1.000000   1 7.000000 1.000000 3.000000
## sdo3   1015 3.062069 2.090433 3.000000   1 7.000000 1.000000 5.000000
## sdo4   1015 2.527094 1.996794 1.000000   1 7.000000 1.000000 4.000000
## sdo5   1015 3.369458 2.163543 3.000000   1 7.000000 1.000000 5.000000
## sdo6   1015 4.307389 2.221931 4.000000   1 7.000000 2.000000 7.000000
## sdo7   1015 1.865025 1.501646 1.000000   1 7.000000 1.000000 2.000000
## sdo8   1015 2.437438 1.763586 2.000000   1 7.000000 1.000000 4.000000
## MEX1   1015 2.228571 1.662017 1.000000   1 7.000000 1.000000 3.000000
## MEX2   1015 4.655172 2.012611 5.000000   1 7.000000 3.000000 7.000000
## MEX3   1015 5.322167 1.894571 6.000000   1 7.000000 4.000000 7.000000
## rwa1   1015 2.814778 1.823358 2.000000   1 7.000000 1.000000 4.000000
## rwa2   1015 3.448276 2.273472 3.000000   1 7.000000 1.000000 6.000000
## rwa3   1015 2.808867 1.883417 2.000000   1 7.000000 1.000000 4.000000
## rwa4   1015 2.555665 1.842573 2.000000   1 7.000000 1.000000 4.000000
## rwa5   1015 2.659113 1.956644 2.000000   1 7.000000 1.000000 4.000000
## rwa6   1015 2.507389 1.882048 1.000000   1 7.000000 1.000000 4.000000
## SDOI   1015 2.501970 1.603742 2.000000   1 7.000000 1.000000 3.500000
## SDOII  1015 2.794581 1.761807 2.500000   1 7.000000 1.000000 4.000000
## SDOIII 1015 3.838424 1.900509 4.000000   1 7.000000 2.500000 5.500000
## SDOIV  1015 2.151232 1.476989 1.500000   1 7.000000 1.000000 3.000000
##               Skew    Kurtosis
## NEG1    0.88174425 -0.51823794
## NEG2    0.08771659 -1.54846305
## NEG3    0.10754259 -1.17272762
## NEG5    0.77273398 -0.64934212
## NEGn    0.87354659 -0.20224616
## DISC    0.42168352 -0.57735149
## MIN     0.17626442 -0.76426732
## MEX    -0.17626442 -0.76426732
## SDO     0.38787069 -0.46235602
## RWA     0.44746486 -0.59010696
## sdo1    0.71171812 -0.68347406
## sdo2    1.31949395  0.44704465
## sdo3    0.55971096 -1.03389821
## sdo4    1.06016930 -0.21270003
## sdo5    0.36899118 -1.26933685
## sdo6   -0.21842121 -1.35949408
## sdo7    1.89395727  2.88510149
## sdo8    1.08960095  0.16615795
## MEX1    1.32176151  0.90327954
## MEX2   -0.33155094 -1.11671341
## MEX3   -0.82184307 -0.47881378
## rwa1    0.66012423 -0.65574790
## rwa2    0.34956373 -1.37686027
## rwa3    0.73225563 -0.65071591
## rwa4    0.90007829 -0.30970443
## rwa5    0.86702345 -0.55134503
## rwa6    0.94573891 -0.36828477
## SDOI    0.93727862  0.07714219
## SDOII   0.68754350 -0.51024139
## SDOIII  0.01756050 -1.04435488
## SDOIV   1.38799904  1.37152716
MOREX%>%
  select(-gender, -age)%>%
  mvn(mvnTest = "dh")
## $multivariateNormality
##             Test   E df p value MVN
## 1 Doornik-Hansen NaN 62     NaN  NA
## 
## $univariateNormality
##            Test  Variable Statistic   p value Normality
## 1  Shapiro-Wilk   NEG1       0.8014  <0.001      NO    
## 2  Shapiro-Wilk   NEG2       0.8460  <0.001      NO    
## 3  Shapiro-Wilk   NEG3       0.9096  <0.001      NO    
## 4  Shapiro-Wilk   NEG5       0.8385  <0.001      NO    
## 5  Shapiro-Wilk   NEGn       0.8375  <0.001      NO    
## 6  Shapiro-Wilk   DISC       0.9617  <0.001      NO    
## 7  Shapiro-Wilk    MIN       0.9736  <0.001      NO    
## 8  Shapiro-Wilk    MEX       0.9736  <0.001      NO    
## 9  Shapiro-Wilk    SDO       0.9680  <0.001      NO    
## 10 Shapiro-Wilk    RWA       0.9553  <0.001      NO    
## 11 Shapiro-Wilk   sdo1       0.8332  <0.001      NO    
## 12 Shapiro-Wilk   sdo2       0.6979  <0.001      NO    
## 13 Shapiro-Wilk   sdo3       0.8440  <0.001      NO    
## 14 Shapiro-Wilk   sdo4       0.7569  <0.001      NO    
## 15 Shapiro-Wilk   sdo5       0.8644  <0.001      NO    
## 16 Shapiro-Wilk   sdo6       0.8732  <0.001      NO    
## 17 Shapiro-Wilk   sdo7       0.6387  <0.001      NO    
## 18 Shapiro-Wilk   sdo8       0.7926  <0.001      NO    
## 19 Shapiro-Wilk   MEX1       0.7526  <0.001      NO    
## 20 Shapiro-Wilk   MEX2       0.8897  <0.001      NO    
## 21 Shapiro-Wilk   MEX3       0.8177  <0.001      NO    
## 22 Shapiro-Wilk   rwa1       0.8575  <0.001      NO    
## 23 Shapiro-Wilk   rwa2       0.8472  <0.001      NO    
## 24 Shapiro-Wilk   rwa3       0.8475  <0.001      NO    
## 25 Shapiro-Wilk   rwa4       0.7979  <0.001      NO    
## 26 Shapiro-Wilk   rwa5       0.8011  <0.001      NO    
## 27 Shapiro-Wilk   rwa6       0.7790  <0.001      NO    
## 28 Shapiro-Wilk   SDOI       0.8551  <0.001      NO    
## 29 Shapiro-Wilk   SDOII      0.8765  <0.001      NO    
## 30 Shapiro-Wilk  SDOIII      0.9368  <0.001      NO    
## 31 Shapiro-Wilk   SDOIV      0.7833  <0.001      NO    
## 
## $Descriptives
##           n     Mean  Std.Dev   Median Min      Max     25th     75th
## NEG1   1015 2.730049 2.018170 2.000000   1 7.000000 1.000000 4.000000
## NEG2   1015 3.827586 2.369020 4.000000   1 7.000000 1.000000 6.000000
## NEG3   1015 3.866010 2.039979 4.000000   1 7.000000 2.000000 6.000000
## NEG5   1015 2.903448 2.000626 2.000000   1 7.000000 1.000000 4.000000
## NEGn   1015 2.725123 1.829063 2.000000   1 7.000000 1.000000 4.000000
## DISC   1015 3.210443 1.527786 3.000000   1 7.000000 2.000000 4.200000
## MIN    1015 3.931363 1.556165 4.000000   1 7.000000 3.000000 5.000000
## MEX    1015 4.068637 1.556165 4.000000   1 7.000000 3.000000 5.000000
## SDO    1015 2.821552 1.230008 2.750000   1 7.000000 1.750000 3.750000
## RWA    1015 2.799015 1.316253 2.666667   1 6.833333 1.666667 3.666667
## sdo1   1015 2.760591 1.899035 2.000000   1 7.000000 1.000000 4.000000
## sdo2   1015 2.243350 1.873434 1.000000   1 7.000000 1.000000 3.000000
## sdo3   1015 3.062069 2.090433 3.000000   1 7.000000 1.000000 5.000000
## sdo4   1015 2.527094 1.996794 1.000000   1 7.000000 1.000000 4.000000
## sdo5   1015 3.369458 2.163543 3.000000   1 7.000000 1.000000 5.000000
## sdo6   1015 4.307389 2.221931 4.000000   1 7.000000 2.000000 7.000000
## sdo7   1015 1.865025 1.501646 1.000000   1 7.000000 1.000000 2.000000
## sdo8   1015 2.437438 1.763586 2.000000   1 7.000000 1.000000 4.000000
## MEX1   1015 2.228571 1.662017 1.000000   1 7.000000 1.000000 3.000000
## MEX2   1015 4.655172 2.012611 5.000000   1 7.000000 3.000000 7.000000
## MEX3   1015 5.322167 1.894571 6.000000   1 7.000000 4.000000 7.000000
## rwa1   1015 2.814778 1.823358 2.000000   1 7.000000 1.000000 4.000000
## rwa2   1015 3.448276 2.273472 3.000000   1 7.000000 1.000000 6.000000
## rwa3   1015 2.808867 1.883417 2.000000   1 7.000000 1.000000 4.000000
## rwa4   1015 2.555665 1.842573 2.000000   1 7.000000 1.000000 4.000000
## rwa5   1015 2.659113 1.956644 2.000000   1 7.000000 1.000000 4.000000
## rwa6   1015 2.507389 1.882048 1.000000   1 7.000000 1.000000 4.000000
## SDOI   1015 2.501970 1.603742 2.000000   1 7.000000 1.000000 3.500000
## SDOII  1015 2.794581 1.761807 2.500000   1 7.000000 1.000000 4.000000
## SDOIII 1015 3.838424 1.900509 4.000000   1 7.000000 2.500000 5.500000
## SDOIV  1015 2.151232 1.476989 1.500000   1 7.000000 1.000000 3.000000
##               Skew    Kurtosis
## NEG1    0.88174425 -0.51823794
## NEG2    0.08771659 -1.54846305
## NEG3    0.10754259 -1.17272762
## NEG5    0.77273398 -0.64934212
## NEGn    0.87354659 -0.20224616
## DISC    0.42168352 -0.57735149
## MIN     0.17626442 -0.76426732
## MEX    -0.17626442 -0.76426732
## SDO     0.38787069 -0.46235602
## RWA     0.44746486 -0.59010696
## sdo1    0.71171812 -0.68347406
## sdo2    1.31949395  0.44704465
## sdo3    0.55971096 -1.03389821
## sdo4    1.06016930 -0.21270003
## sdo5    0.36899118 -1.26933685
## sdo6   -0.21842121 -1.35949408
## sdo7    1.89395727  2.88510149
## sdo8    1.08960095  0.16615795
## MEX1    1.32176151  0.90327954
## MEX2   -0.33155094 -1.11671341
## MEX3   -0.82184307 -0.47881378
## rwa1    0.66012423 -0.65574790
## rwa2    0.34956373 -1.37686027
## rwa3    0.73225563 -0.65071591
## rwa4    0.90007829 -0.30970443
## rwa5    0.86702345 -0.55134503
## rwa6    0.94573891 -0.36828477
## SDOI    0.93727862  0.07714219
## SDOII   0.68754350 -0.51024139
## SDOIII  0.01756050 -1.04435488
## SDOIV   1.38799904  1.37152716
MOREX%>%
  select(-gender, -age)%>%
  mvn(mvnTest = "mardia")
## $multivariateNormality
##              Test         Statistic p value Result
## 1 Mardia Skewness -1778.53345490144       1    YES
## 2 Mardia Kurtosis  -142.28285335133       0     NO
## 3             MVN              <NA>    <NA>     NO
## 
## $univariateNormality
##            Test  Variable Statistic   p value Normality
## 1  Shapiro-Wilk   NEG1       0.8014  <0.001      NO    
## 2  Shapiro-Wilk   NEG2       0.8460  <0.001      NO    
## 3  Shapiro-Wilk   NEG3       0.9096  <0.001      NO    
## 4  Shapiro-Wilk   NEG5       0.8385  <0.001      NO    
## 5  Shapiro-Wilk   NEGn       0.8375  <0.001      NO    
## 6  Shapiro-Wilk   DISC       0.9617  <0.001      NO    
## 7  Shapiro-Wilk    MIN       0.9736  <0.001      NO    
## 8  Shapiro-Wilk    MEX       0.9736  <0.001      NO    
## 9  Shapiro-Wilk    SDO       0.9680  <0.001      NO    
## 10 Shapiro-Wilk    RWA       0.9553  <0.001      NO    
## 11 Shapiro-Wilk   sdo1       0.8332  <0.001      NO    
## 12 Shapiro-Wilk   sdo2       0.6979  <0.001      NO    
## 13 Shapiro-Wilk   sdo3       0.8440  <0.001      NO    
## 14 Shapiro-Wilk   sdo4       0.7569  <0.001      NO    
## 15 Shapiro-Wilk   sdo5       0.8644  <0.001      NO    
## 16 Shapiro-Wilk   sdo6       0.8732  <0.001      NO    
## 17 Shapiro-Wilk   sdo7       0.6387  <0.001      NO    
## 18 Shapiro-Wilk   sdo8       0.7926  <0.001      NO    
## 19 Shapiro-Wilk   MEX1       0.7526  <0.001      NO    
## 20 Shapiro-Wilk   MEX2       0.8897  <0.001      NO    
## 21 Shapiro-Wilk   MEX3       0.8177  <0.001      NO    
## 22 Shapiro-Wilk   rwa1       0.8575  <0.001      NO    
## 23 Shapiro-Wilk   rwa2       0.8472  <0.001      NO    
## 24 Shapiro-Wilk   rwa3       0.8475  <0.001      NO    
## 25 Shapiro-Wilk   rwa4       0.7979  <0.001      NO    
## 26 Shapiro-Wilk   rwa5       0.8011  <0.001      NO    
## 27 Shapiro-Wilk   rwa6       0.7790  <0.001      NO    
## 28 Shapiro-Wilk   SDOI       0.8551  <0.001      NO    
## 29 Shapiro-Wilk   SDOII      0.8765  <0.001      NO    
## 30 Shapiro-Wilk  SDOIII      0.9368  <0.001      NO    
## 31 Shapiro-Wilk   SDOIV      0.7833  <0.001      NO    
## 
## $Descriptives
##           n     Mean  Std.Dev   Median Min      Max     25th     75th
## NEG1   1015 2.730049 2.018170 2.000000   1 7.000000 1.000000 4.000000
## NEG2   1015 3.827586 2.369020 4.000000   1 7.000000 1.000000 6.000000
## NEG3   1015 3.866010 2.039979 4.000000   1 7.000000 2.000000 6.000000
## NEG5   1015 2.903448 2.000626 2.000000   1 7.000000 1.000000 4.000000
## NEGn   1015 2.725123 1.829063 2.000000   1 7.000000 1.000000 4.000000
## DISC   1015 3.210443 1.527786 3.000000   1 7.000000 2.000000 4.200000
## MIN    1015 3.931363 1.556165 4.000000   1 7.000000 3.000000 5.000000
## MEX    1015 4.068637 1.556165 4.000000   1 7.000000 3.000000 5.000000
## SDO    1015 2.821552 1.230008 2.750000   1 7.000000 1.750000 3.750000
## RWA    1015 2.799015 1.316253 2.666667   1 6.833333 1.666667 3.666667
## sdo1   1015 2.760591 1.899035 2.000000   1 7.000000 1.000000 4.000000
## sdo2   1015 2.243350 1.873434 1.000000   1 7.000000 1.000000 3.000000
## sdo3   1015 3.062069 2.090433 3.000000   1 7.000000 1.000000 5.000000
## sdo4   1015 2.527094 1.996794 1.000000   1 7.000000 1.000000 4.000000
## sdo5   1015 3.369458 2.163543 3.000000   1 7.000000 1.000000 5.000000
## sdo6   1015 4.307389 2.221931 4.000000   1 7.000000 2.000000 7.000000
## sdo7   1015 1.865025 1.501646 1.000000   1 7.000000 1.000000 2.000000
## sdo8   1015 2.437438 1.763586 2.000000   1 7.000000 1.000000 4.000000
## MEX1   1015 2.228571 1.662017 1.000000   1 7.000000 1.000000 3.000000
## MEX2   1015 4.655172 2.012611 5.000000   1 7.000000 3.000000 7.000000
## MEX3   1015 5.322167 1.894571 6.000000   1 7.000000 4.000000 7.000000
## rwa1   1015 2.814778 1.823358 2.000000   1 7.000000 1.000000 4.000000
## rwa2   1015 3.448276 2.273472 3.000000   1 7.000000 1.000000 6.000000
## rwa3   1015 2.808867 1.883417 2.000000   1 7.000000 1.000000 4.000000
## rwa4   1015 2.555665 1.842573 2.000000   1 7.000000 1.000000 4.000000
## rwa5   1015 2.659113 1.956644 2.000000   1 7.000000 1.000000 4.000000
## rwa6   1015 2.507389 1.882048 1.000000   1 7.000000 1.000000 4.000000
## SDOI   1015 2.501970 1.603742 2.000000   1 7.000000 1.000000 3.500000
## SDOII  1015 2.794581 1.761807 2.500000   1 7.000000 1.000000 4.000000
## SDOIII 1015 3.838424 1.900509 4.000000   1 7.000000 2.500000 5.500000
## SDOIV  1015 2.151232 1.476989 1.500000   1 7.000000 1.000000 3.000000
##               Skew    Kurtosis
## NEG1    0.88174425 -0.51823794
## NEG2    0.08771659 -1.54846305
## NEG3    0.10754259 -1.17272762
## NEG5    0.77273398 -0.64934212
## NEGn    0.87354659 -0.20224616
## DISC    0.42168352 -0.57735149
## MIN     0.17626442 -0.76426732
## MEX    -0.17626442 -0.76426732
## SDO     0.38787069 -0.46235602
## RWA     0.44746486 -0.59010696
## sdo1    0.71171812 -0.68347406
## sdo2    1.31949395  0.44704465
## sdo3    0.55971096 -1.03389821
## sdo4    1.06016930 -0.21270003
## sdo5    0.36899118 -1.26933685
## sdo6   -0.21842121 -1.35949408
## sdo7    1.89395727  2.88510149
## sdo8    1.08960095  0.16615795
## MEX1    1.32176151  0.90327954
## MEX2   -0.33155094 -1.11671341
## MEX3   -0.82184307 -0.47881378
## rwa1    0.66012423 -0.65574790
## rwa2    0.34956373 -1.37686027
## rwa3    0.73225563 -0.65071591
## rwa4    0.90007829 -0.30970443
## rwa5    0.86702345 -0.55134503
## rwa6    0.94573891 -0.36828477
## SDOI    0.93727862  0.07714219
## SDOII   0.68754350 -0.51024139
## SDOIII  0.01756050 -1.04435488
## SDOIV   1.38799904  1.37152716

Correlation Analyses

Correlation Between Sets of Items Representing the Dependent variable; discriminatory attitudes against the Roma.

MOREX%>% 
  select(NEG1, NEG2, NEG3, NEG5, NEGn)%>% 
  lowerCor()%>%
  corPlot()
##      NEG1 NEG2 NEG3 NEG5 NEGn
## NEG1 1.00                    
## NEG2 0.57 1.00               
## NEG3 0.34 0.32 1.00          
## NEG5 0.46 0.43 0.46 1.00     
## NEGn 0.44 0.42 0.43 0.59 1.00

cor.test(MOREX$NEG1 , MOREX$NEG2)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$NEG1 and MOREX$NEG2
## t = 21.872, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5230627 0.6067544
## sample estimates:
##       cor 
## 0.5663669
ggplot(MOREX) +
  aes(x = NEG1 , y = NEG2) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$NEG1 , MOREX$NEG3)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$NEG1 and MOREX$NEG3
## t = 11.323, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2794153 0.3887016
## sample estimates:
##       cor 
## 0.3351854
ggplot(MOREX) +
  aes(x = NEG1 , y = NEG3) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$NEG1 , MOREX$NEG5)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$NEG1 and MOREX$NEG5
## t = 16.303, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4057574 0.5033208
## sample estimates:
##       cor 
## 0.4559076
ggplot(MOREX) +
  aes(x = NEG1 , y = NEG5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$NEG1 , MOREX$NEGn)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$NEG1 and MOREX$NEGn
## t = 15.463, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3858352 0.4854723
## sample estimates:
##       cor 
## 0.4369933
ggplot(MOREX) +
  aes(x = NEG1 , y = NEGn) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$NEG2 , MOREX$NEG3)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$NEG2 and MOREX$NEG3
## t = 10.929, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2686143 0.3787433
## sample estimates:
##       cor 
## 0.3247793
ggplot(MOREX) +
  aes(x = NEG2 , y = NEG3) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor(MOREX$NEG2 , MOREX$NEG5)
## [1] 0.427415
ggplot(MOREX) +
  aes(x = NEG2 , y = NEG5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$NEG2 , MOREX$NEGn)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$NEG2 and MOREX$NEGn
## t = 14.715, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3676235 0.4690833
## sample estimates:
##       cor 
## 0.4196634
ggplot(MOREX) +
  aes(x = NEG2 , y = NEGn) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$NEG3 , MOREX$NEG5)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$NEG3 and MOREX$NEG5
## t = 16.557, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4116595 0.5085927
## sample estimates:
##       cor 
## 0.4615024
ggplot(MOREX) +
  aes(x = NEG3 , y = NEG5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$NEG3 , MOREX$NEGn)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$NEG3 and MOREX$NEGn
## t = 14.973, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3739587 0.4747923
## sample estimates:
##       cor 
## 0.4256961
ggplot(MOREX) +
  aes(x = NEG3 , y = NEGn) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$NEG5 , MOREX$NEGn)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$NEG5 and MOREX$NEGn
## t = 23.142, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5463134 0.6269246
## sample estimates:
##       cor 
## 0.5880775
ggplot(MOREX) +
  aes(x = NEG5 , y = NEGn) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

Correlation Between Sets of Items Representing the independent variables; RWA and SDO.

RWA

MOREX%>% 
  select(rwa1, rwa2, rwa3, rwa4, rwa5, rwa6)%>% 
  lowerCor()%>%
  corPlot()
##      rwa1 rwa2 rwa3 rwa4 rwa5 rwa6
## rwa1 1.00                         
## rwa2 0.29 1.00                    
## rwa3 0.26 0.70 1.00               
## rwa4 0.37 0.25 0.25 1.00          
## rwa5 0.52 0.34 0.32 0.50 1.00     
## rwa6 0.23 0.27 0.28 0.30 0.32 1.00

cor.test(MOREX$rwa1 , MOREX$rwa2)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa1 and MOREX$rwa2
## t = 9.5866, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2309703 0.3438358
## sample estimates:
##       cor 
## 0.2884045
ggplot(MOREX) +
  aes(x = rwa1 , y = rwa2) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa1 , MOREX$rwa3)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa1 and MOREX$rwa3
## t = 8.5769, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2018975 0.3166612
## sample estimates:
##       cor 
## 0.2601981
ggplot(MOREX) +
  aes(x = rwa1 , y = rwa3) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa1 , MOREX$rwa4)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa1 and MOREX$rwa4
## t = 12.676, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3156487 0.4219226
## sample estimates:
##       cor 
## 0.3699954
ggplot(MOREX) +
  aes(x = rwa1 , y = rwa4) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa1 , MOREX$rwa5)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa1 and MOREX$rwa5
## t = 19.472, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4756074 0.5652495
## sample estimates:
##       cor 
## 0.5218677
ggplot(MOREX) +
  aes(x = rwa1 , y = rwa5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa1 , MOREX$rwa6)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa1 and MOREX$rwa6
## t = 7.5028, df = 1013, p-value = 1.364e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1703141 0.2869248
## sample estimates:
##       cor 
## 0.2294426
ggplot(MOREX) +
  aes(x = rwa1 , y = rwa6) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa2 , MOREX$rwa3)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa2 and MOREX$rwa3
## t = 31.434, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6701319 0.7325481
## sample estimates:
##       cor 
## 0.7026894
ggplot(MOREX) +
  aes(x = rwa2 , y = rwa3) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa2 , MOREX$rwa4)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa2 and MOREX$rwa4
## t = 8.3439, df = 1013, p-value = 2.332e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1951019 0.3102820
## sample estimates:
##       cor 
## 0.2535906
ggplot(MOREX) +
  aes(x = rwa2 , y = rwa4) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa2 , MOREX$rwa5)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa2 and MOREX$rwa5
## t = 11.376, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2808427 0.3900157
## sample estimates:
##       cor 
## 0.3365597
ggplot(MOREX) +
  aes(x = rwa2 , y = rwa5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa2 , MOREX$rwa6)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa2 and MOREX$rwa6
## t = 8.8075, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2085925 0.3229358
## sample estimates:
##       cor 
## 0.2667024
ggplot(MOREX) +
  aes(x = rwa2 , y = rwa6) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa3 , MOREX$rwa4)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa3 and MOREX$rwa4
## t = 8.0594, df = 1013, p-value = 2.146e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1867609 0.3024379
## sample estimates:
##      cor 
## 0.245473
ggplot(MOREX) +
  aes(x = rwa3 , y = rwa4) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa3 , MOREX$rwa5)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa3 and MOREX$rwa5
## t = 10.932, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2686818 0.3788056
## sample estimates:
##       cor 
## 0.3248443
ggplot(MOREX) +
  aes(x = rwa3 , y = rwa5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa3 , MOREX$rwa6)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa3 and MOREX$rwa6
## t = 9.4036, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2257492 0.3389694
## sample estimates:
##       cor 
## 0.2833463
ggplot(MOREX) +
  aes(x = rwa3 , y = rwa6) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa4 , MOREX$rwa5)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa4 and MOREX$rwa5
## t = 18.529, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4556911 0.5476942
## sample estimates:
##       cor 
## 0.5031168
ggplot(MOREX) +
  aes(x = rwa4 , y = rwa5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa4 , MOREX$rwa6)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa4 and MOREX$rwa6
## t = 9.9565, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2414594 0.3535938
## sample estimates:
##       cor 
## 0.2985566
ggplot(MOREX) +
  aes(x = rwa4 , y = rwa6) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$rwa5 , MOREX$rwa6)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$rwa5 and MOREX$rwa6
## t = 10.857, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2666184 0.3769003
## sample estimates:
##       cor 
## 0.3228548
ggplot(MOREX) +
  aes(x = rwa5 , y = rwa6) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

SDO

MOREX%>% 
  select(sdo1, sdo2, sdo3, sdo4, sdo5, sdo6, sdo7, sdo8)%>% 
  lowerCor()%>%
  corPlot()
##      sdo1 sdo2 sdo3 sdo4 sdo5 sdo6 sdo7 sdo8
## sdo1 1.00                                   
## sdo2 0.45 1.00                              
## sdo3 0.33 0.27 1.00                         
## sdo4 0.34 0.23 0.49 1.00                    
## sdo5 0.41 0.30 0.37 0.25 1.00               
## sdo6 0.38 0.24 0.29 0.22 0.50 1.00          
## sdo7 0.15 0.24 0.29 0.30 0.13 0.03 1.00     
## sdo8 0.34 0.30 0.44 0.36 0.29 0.23 0.63 1.00

cor.test(MOREX$sdo1 , MOREX$sdo2)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo1 and MOREX$sdo2
## t = 15.85, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3950757 0.4937614
## sample estimates:
##      cor 
## 0.445772
ggplot(MOREX) +
  aes(x = sdo1 , y = sdo2) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo1 , MOREX$sdo3)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo1 and MOREX$sdo3
## t = 11.049, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2718931 0.3817689
## sample estimates:
##       cor 
## 0.3279396
ggplot(MOREX) +
  aes(x = sdo1 , y = sdo3) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo1 , MOREX$sdo4)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo1 and MOREX$sdo4
## t = 11.524, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2848930 0.3937422
## sample estimates:
##       cor 
## 0.3404578
ggplot(MOREX) +
  aes(x = sdo1 , y = sdo4) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo1 , MOREX$sdo5)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo1 and MOREX$sdo5
## t = 14.153, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3536237 0.4564369
## sample estimates:
##       cor 
## 0.4063156
ggplot(MOREX) +
  aes(x = sdo1 , y = sdo5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo1 , MOREX$sdo6)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo1 and MOREX$sdo6
## t = 12.886, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3211689 0.4269590
## sample estimates:
##       cor 
## 0.3752854
ggplot(MOREX) +
  aes(x = sdo1 , y = sdo6) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo1 , MOREX$sdo7)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo1 and MOREX$sdo7
## t = 4.8682, df = 1013, p-value = 1.306e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.09050577 0.21076903
## sample estimates:
##       cor 
## 0.1511968
ggplot(MOREX) +
  aes(x = sdo1 , y = sdo7) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo1 , MOREX$sdo8)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo1 and MOREX$sdo8
## t = 11.548, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2855367 0.3943341
## sample estimates:
##       cor 
## 0.3410771
ggplot(MOREX) +
  aes(x = sdo1 , y = sdo8) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo2 , MOREX$sdo3)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo2 and MOREX$sdo3
## t = 8.9112, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2115913 0.3257430
## sample estimates:
##      cor 
## 0.269614
ggplot(MOREX) +
  aes(x = sdo2 , y = sdo3) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo2 , MOREX$sdo4)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo2 and MOREX$sdo4
## t = 7.5982, df = 1013, p-value = 6.817e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1731459 0.2896002
## sample estimates:
##      cor 
## 0.232205
ggplot(MOREX) +
  aes(x = sdo2 , y = sdo4) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo2 , MOREX$sdo5)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo2 and MOREX$sdo5
## t = 10.177, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2476687 0.3593589
## sample estimates:
##       cor 
## 0.3045604
ggplot(MOREX) +
  aes(x = sdo2 , y = sdo5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo2 , MOREX$sdo6)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo2 and MOREX$sdo6
## t = 7.8856, df = 1013, p-value = 8.069e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1816415 0.2976157
## sample estimates:
##       cor 
## 0.2404867
ggplot(MOREX) +
  aes(x = sdo2 , y = sdo6) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo2 , MOREX$sdo7)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo2 and MOREX$sdo7
## t = 7.7433, df = 1013, p-value = 2.341e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1774408 0.2936545
## sample estimates:
##       cor 
## 0.2363929
ggplot(MOREX) +
  aes(x = sdo2 , y = sdo7) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo2 , MOREX$sdo8)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo2 and MOREX$sdo8
## t = 9.9753, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2419912 0.3540879
## sample estimates:
##      cor 
## 0.299071
ggplot(MOREX) +
  aes(x = sdo2 , y = sdo8) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo3 , MOREX$sdo4)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo3 and MOREX$sdo4
## t = 17.707, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4377392 0.5318006
## sample estimates:
##       cor 
## 0.4861769
ggplot(MOREX) +
  aes(x = sdo3 , y = sdo4) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo3 , MOREX$sdo5)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo3 and MOREX$sdo5
## t = 12.528, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3117605 0.4183713
## sample estimates:
##       cor 
## 0.3662673
ggplot(MOREX) +
  aes(x = sdo3 , y = sdo5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo3 , MOREX$sdo6)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo3 and MOREX$sdo6
## t = 9.6274, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2321322 0.3449179
## sample estimates:
##       cor 
## 0.2895297
ggplot(MOREX) +
  aes(x = sdo3 , y = sdo6) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo3 , MOREX$sdo7)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo3 and MOREX$sdo7
## t = 9.7292, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2350266 0.3476122
## sample estimates:
##      cor 
## 0.292332
ggplot(MOREX) +
  aes(x = sdo3 , y = sdo7) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo3 , MOREX$sdo8)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo3 and MOREX$sdo8
## t = 15.414, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3846651 0.4844214
## sample estimates:
##      cor 
## 0.435881
ggplot(MOREX) +
  aes(x = sdo3 , y = sdo8) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo4 , MOREX$sdo5)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo4 and MOREX$sdo5
## t = 8.0835, df = 1013, p-value = 1.784e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1874673 0.3031028
## sample estimates:
##       cor 
## 0.2461608
ggplot(MOREX) +
  aes(x = sdo4 , y = sdo5) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo4 , MOREX$sdo6)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo4 and MOREX$sdo6
## t = 7.2145, df = 1013, p-value = 1.059e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1617338 0.2788073
## sample estimates:
##       cor 
## 0.2210668
ggplot(MOREX) +
  aes(x = sdo4 , y = sdo6) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo4 , MOREX$sdo7)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo4 and MOREX$sdo7
## t = 9.9259, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2405971 0.3527925
## sample estimates:
##       cor 
## 0.2977225
ggplot(MOREX) +
  aes(x = sdo4 , y = sdo7) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo4 , MOREX$sdo8)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo4 and MOREX$sdo8
## t = 12.188, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3027417 0.4101213
## sample estimates:
##       cor 
## 0.3576129
ggplot(MOREX) +
  aes(x = sdo4 , y = sdo8) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo5 , MOREX$sdo6)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo5 and MOREX$sdo6
## t = 18.491, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4548779 0.5469757
## sample estimates:
##       cor 
## 0.5023502
ggplot(MOREX) +
  aes(x = sdo5 , y = sdo6) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo5 , MOREX$sdo7)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo5 and MOREX$sdo7
## t = 4.018, df = 1013, p-value = 6.303e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06421093 0.18535374
## sample estimates:
##       cor 
## 0.1252492
ggplot(MOREX) +
  aes(x = sdo5 , y = sdo7) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo5 , MOREX$sdo8)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo5 and MOREX$sdo8
## t = 9.6628, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2331376 0.3458540
## sample estimates:
##       cor 
## 0.2905032
ggplot(MOREX) +
  aes(x = sdo5 , y = sdo8) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo6 , MOREX$sdo7)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo6 and MOREX$sdo7
## t = 0.85744, df = 1013, p-value = 0.3914
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.03466024  0.08831705
## sample estimates:
##       cor 
## 0.0269303
ggplot(MOREX) +
  aes(x = sdo6 , y = sdo7) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo6 , MOREX$sdo8)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo6 and MOREX$sdo8
## t = 7.6406, df = 1013, p-value = 4.996e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1744026 0.2907870
## sample estimates:
##       cor 
## 0.2334306
ggplot(MOREX) +
  aes(x = sdo6 , y = sdo8) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$sdo7 , MOREX$sdo8)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$sdo7 and MOREX$sdo8
## t = 26.129, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5962743 0.6699034
## sample estimates:
##       cor 
## 0.6345263
ggplot(MOREX) +
  aes(x = sdo7 , y = sdo8) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

Correlation Between Items Representing the mediating variable; MEX.

MOREX%>% 
  select(MEX1, MEX2, MEX3)%>% 
  lowerCor()%>%
  corPlot()
##      MEX1 MEX2 MEX3
## MEX1 1.00          
## MEX2 0.46 1.00     
## MEX3 0.40 0.76 1.00

cor.test(MOREX$MEX1 , MOREX$MEX2)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$MEX1 and MOREX$MEX2
## t = 16.66, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4140449 0.5107213
## sample estimates:
##       cor 
## 0.4637625
ggplot(MOREX) +
  aes(x = MEX1 , y = MEX2) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$MEX1 , MOREX$MEX3)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$MEX1 and MOREX$MEX3
## t = 13.686, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3417952 0.4457194
## sample estimates:
##       cor 
## 0.3950203
ggplot(MOREX) +
  aes(x = MEX1 , y = MEX3) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

cor.test(MOREX$MEX2 , MOREX$MEX3)
## 
##  Pearson's product-moment correlation
## 
## data:  MOREX$MEX2 and MOREX$MEX3
## t = 37.317, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7336640 0.7856021
## sample estimates:
##       cor 
## 0.7608489
ggplot(MOREX) +
  aes(x = MEX2 , y = MEX3) +
  geom_point(position = "jitter") + 
  geom_smooth(method="lm")

Exploratory Factor Analyses

By running exploratory factor analyses, we decided to parcel the questions of SDO scale in a following manner: SDOI as sdo1 with sdo2, SDOII as sdo3 with sdo4, SDOIII as sdo5 with sdo6, and SDOIV as sdo7 and sdo8. Moreover, the RWA scale was divided into two disticnt scales. RWAMIN containing rwa2 and rwa3 representing items addressing prejudice against the minority groups. And GENRWA containing the rest of the items representing general right wing athoritarian propensity.

SDO Scale

MOREX%>%
  select(sdo1, sdo2, sdo3, sdo4, sdo5, sdo6, sdo7, sdo8)%>%
  factanal(factors=1, scores="regression")
## 
## Call:
## factanal(x = ., factors = 1, scores = "regression")
## 
## Uniquenesses:
##  sdo1  sdo2  sdo3  sdo4  sdo5  sdo6  sdo7  sdo8 
## 0.644 0.746 0.582 0.676 0.693 0.777 0.766 0.558 
## 
## Loadings:
##      Factor1
## sdo1 0.596  
## sdo2 0.504  
## sdo3 0.646  
## sdo4 0.569  
## sdo5 0.555  
## sdo6 0.472  
## sdo7 0.484  
## sdo8 0.665  
## 
##                Factor1
## SS loadings      2.558
## Proportion Var   0.320
## 
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 644.06 on 20 degrees of freedom.
## The p-value is 1.47e-123
MOREX%>%
  select(sdo1, sdo2, sdo3, sdo4, sdo5, sdo6, sdo7, sdo8)%>%
  factanal(factors=2, scores="regression")
## 
## Call:
## factanal(x = ., factors = 2, scores = "regression")
## 
## Uniquenesses:
##  sdo1  sdo2  sdo3  sdo4  sdo5  sdo6  sdo7  sdo8 
## 0.583 0.746 0.629 0.719 0.537 0.580 0.307 0.344 
## 
## Loadings:
##      Factor1 Factor2
## sdo1 0.617   0.192  
## sdo2 0.435   0.254  
## sdo3 0.480   0.375  
## sdo4 0.390   0.359  
## sdo5 0.670   0.118  
## sdo6 0.648          
## sdo7         0.832  
## sdo8 0.307   0.750  
## 
##                Factor1 Factor2
## SS loadings      1.915   1.640
## Proportion Var   0.239   0.205
## Cumulative Var   0.239   0.444
## 
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 170.64 on 13 degrees of freedom.
## The p-value is 1.37e-29
MOREX%>%
  select(sdo1, sdo2, sdo3, sdo4, sdo5, sdo6, sdo7, sdo8)%>%
  factanal(factors=3, scores="regression")
## 
## Call:
## factanal(x = ., factors = 3, scores = "regression")
## 
## Uniquenesses:
##  sdo1  sdo2  sdo3  sdo4  sdo5  sdo6  sdo7  sdo8 
## 0.601 0.752 0.610 0.005 0.498 0.552 0.351 0.301 
## 
## Loadings:
##      Factor1 Factor2 Factor3
## sdo1 0.576   0.166   0.199  
## sdo2 0.424   0.239   0.103  
## sdo3 0.404   0.316   0.356  
## sdo4 0.204   0.200   0.956  
## sdo5 0.695   0.102          
## sdo6 0.663                  
## sdo7         0.793   0.141  
## sdo8 0.305   0.764   0.149  
## 
##                Factor1 Factor2 Factor3
## SS loadings      1.734   1.447   1.148
## Proportion Var   0.217   0.181   0.144
## Cumulative Var   0.217   0.398   0.541
## 
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 81.78 on 7 degrees of freedom.
## The p-value is 5.97e-15
MOREX%>%
  select(sdo1, sdo2, sdo3, sdo4, sdo5, sdo6, sdo7, sdo8)%>%
  factanal(factors=4, scores="regression")
## 
## Call:
## factanal(x = ., factors = 4, scores = "regression")
## 
## Uniquenesses:
##  sdo1  sdo2  sdo3  sdo4  sdo5  sdo6  sdo7  sdo8 
## 0.024 0.740 0.005 0.697 0.498 0.481 0.119 0.436 
## 
## Loadings:
##      Factor1 Factor2 Factor3 Factor4
## sdo1          0.150   0.932   0.276 
## sdo2  0.220   0.139   0.361   0.249 
## sdo3  0.189   0.948   0.116   0.219 
## sdo4  0.260   0.394   0.226   0.170 
## sdo5  0.114   0.190   0.205   0.641 
## sdo6          0.120   0.177   0.688 
## sdo7  0.928   0.123                 
## sdo8  0.643   0.259   0.199   0.211 
## 
##                Factor1 Factor2 Factor3 Factor4
## SS loadings      1.448   1.228   1.181   1.144
## Proportion Var   0.181   0.153   0.148   0.143
## Cumulative Var   0.181   0.334   0.482   0.625
## 
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 3.52 on 2 degrees of freedom.
## The p-value is 0.172

RWA Scale

MOREX%>%
  select(rwa1, rwa2, rwa3, rwa4, rwa5, rwa6)%>%
  factanal(factors=1, scores="regression")
## 
## Call:
## factanal(x = ., factors = 1, scores = "regression")
## 
## Uniquenesses:
##  rwa1  rwa2  rwa3  rwa4  rwa5  rwa6 
## 0.797 0.376 0.392 0.814 0.717 0.837 
## 
## Loadings:
##      Factor1
## rwa1 0.451  
## rwa2 0.790  
## rwa3 0.780  
## rwa4 0.431  
## rwa5 0.532  
## rwa6 0.404  
## 
##                Factor1
## SS loadings      2.068
## Proportion Var   0.345
## 
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 466.74 on 9 degrees of freedom.
## The p-value is 7.55e-95
MOREX%>%
  select(rwa1, rwa2, rwa3, rwa4, rwa5, rwa6)%>%
  factanal(factors=2, scores="regression")
## 
## Call:
## factanal(x = ., factors = 2, scores = "regression")
## 
## Uniquenesses:
##  rwa1  rwa2  rwa3  rwa4  rwa5  rwa6 
## 0.611 0.380 0.195 0.624 0.318 0.814 
## 
## Loadings:
##      Factor1 Factor2
## rwa1 0.601   0.166  
## rwa2 0.251   0.746  
## rwa3 0.200   0.874  
## rwa4 0.595   0.145  
## rwa5 0.805   0.185  
## rwa6 0.357   0.241  
## 
##                Factor1 Factor2
## SS loadings      1.594   1.463
## Proportion Var   0.266   0.244
## Cumulative Var   0.266   0.509
## 
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 8.68 on 4 degrees of freedom.
## The p-value is 0.0697
MOREX%>%
  select(rwa1, rwa2, rwa3, rwa4, rwa5, rwa6)%>%
  factanal(factors=3, scores="regression")
## 
## Call:
## factanal(x = ., factors = 3, scores = "regression")
## 
## Uniquenesses:
##  rwa1  rwa2  rwa3  rwa4  rwa5  rwa6 
## 0.400 0.378 0.196 0.527 0.410 0.786 
## 
## Loadings:
##      Factor1 Factor2 Factor3
## rwa1 0.150   0.307   0.695  
## rwa2 0.744   0.204   0.164  
## rwa3 0.870   0.190   0.103  
## rwa4 0.116   0.640   0.224  
## rwa5 0.191   0.595   0.446  
## rwa6 0.228   0.387   0.110  
## 
##                Factor1 Factor2 Factor3
## SS loadings      1.434   1.086   0.782
## Proportion Var   0.239   0.181   0.130
## Cumulative Var   0.239   0.420   0.550
## 
## The degrees of freedom for the model is 0 and the fit was 0

Hypothesis 1

It was hypothesized that right-wing authoritarianism and social dominance orientation will predict negative intergroup attitudes against the Roma.

Confirmatory Factor Analysis

Confirmatory factor analysis was computed for the structural model. The errors of two pairs of items were allowed to covarry in order to improve the model fit. The model \(x^{2}\) of 407.497 showed no absolute fit (p < .001), while other measures confirmed that the model fit is overally satisfactory: CFI = .928; TLI = .908; SRMR = .053; and RMSEA = .063 and 90% CI = .057 - .069.

Model Specification

MORAL_MODEL1 <- " 
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6"
FIT_MORAL_MODEL1 <- cfa(MORAL_MODEL1, data=MOREX, std.lv=TRUE,missing="fiml", estimator="MLR")

Model Results and Plot

summary(FIT_MORAL_MODEL1, fit.measures=TRUE, standardized=TRUE)
## lavaan (0.6-1) converged normally after  28 iterations
## 
##   Number of observations                          1015
##   Number of missing patterns                         1
## 
##   Estimator                                         ML      Robust
##   Model Fit Test Statistic                     514.547     429.413
##   Degrees of freedom                                84          84
##   P-value (Chi-square)                           0.000       0.000
##   Scaling correction factor                                  1.198
##     for the Yuan-Bentler correction (Mplus variant)
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             4643.668    3640.092
##   Degrees of freedom                               105         105
##   P-value                                        0.000       0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.905       0.902
##   Tucker-Lewis Index (TLI)                       0.881       0.878
## 
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -29291.015  -29291.015
##   Loglikelihood unrestricted model (H1)     -29033.742  -29033.742
## 
##   Number of free parameters                         51          51
##   Akaike (AIC)                               58684.030   58684.030
##   Bayesian (BIC)                             58935.085   58935.085
##   Sample-size adjusted Bayesian (BIC)        58773.104   58773.104
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.071       0.064
##   90 Percent Confidence Interval          0.065  0.077       0.058  0.069
##   P-value RMSEA <= 0.05                          0.000       0.000
## 
##   Robust RMSEA                                                  NA
##   90 Percent Confidence Interval                                NA     NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.056       0.056
## 
## Parameter Estimates:
## 
##   Information                                 Observed
##   Observed information based on                Hessian
##   Standard Errors                   Robust.huber.white
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   DISC =~                                                               
##     NEG1              1.365    0.069   19.766    0.000    1.365    0.677
##     NEG2              1.562    0.070   22.295    0.000    1.562    0.660
##     NEG3              1.194    0.068   17.581    0.000    1.194    0.586
##     NEG5              1.459    0.062   23.471    0.000    1.459    0.729
##     NEGn              1.268    0.062   20.569    0.000    1.268    0.694
##   SDO =~                                                                
##     SDOI              1.090    0.059   18.607    0.000    1.090    0.680
##     SDOII             1.126    0.063   17.831    0.000    1.126    0.640
##     SDOIII            1.091    0.066   16.494    0.000    1.091    0.574
##     SDOIV             0.824    0.061   13.489    0.000    0.824    0.558
##   MINRWA =~                                                             
##     rwa2              1.815    0.063   28.890    0.000    1.815    0.799
##     rwa3              1.656    0.056   29.333    0.000    1.656    0.880
##   GENRWA =~                                                             
##     rwa1              1.140    0.062   18.516    0.000    1.140    0.626
##     rwa4              1.151    0.062   18.472    0.000    1.151    0.625
##     rwa5              1.552    0.063   24.671    0.000    1.552    0.794
##     rwa6              0.833    0.073   11.364    0.000    0.833    0.443
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   DISC ~~                                                               
##     SDO               0.441    0.040   11.085    0.000    0.441    0.441
##     MINRWA            0.499    0.035   14.390    0.000    0.499    0.499
##     GENRWA            0.362    0.044    8.200    0.000    0.362    0.362
##   SDO ~~                                                                
##     MINRWA            0.425    0.039   10.834    0.000    0.425    0.425
##     GENRWA            0.420    0.042   10.089    0.000    0.420    0.420
##   MINRWA ~~                                                             
##     GENRWA            0.506    0.038   13.284    0.000    0.506    0.506
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NEG1              2.730    0.063   43.118    0.000    2.730    1.353
##    .NEG2              3.828    0.074   51.500    0.000    3.828    1.616
##    .NEG3              3.866    0.064   60.407    0.000    3.866    1.896
##    .NEG5              2.903    0.063   46.259    0.000    2.903    1.452
##    .NEGn              2.725    0.057   47.490    0.000    2.725    1.491
##    .SDOI              2.502    0.050   49.727    0.000    2.502    1.561
##    .SDOII             2.795    0.055   50.560    0.000    2.795    1.587
##    .SDOIII            3.838    0.060   64.377    0.000    3.838    2.021
##    .SDOIV             2.151    0.046   46.426    0.000    2.151    1.457
##    .rwa2              3.448    0.071   48.346    0.000    3.448    1.517
##    .rwa3              2.809    0.059   47.537    0.000    2.809    1.492
##    .rwa1              2.815    0.057   49.206    0.000    2.815    1.544
##    .rwa4              2.556    0.058   44.211    0.000    2.556    1.388
##    .rwa5              2.659    0.061   43.318    0.000    2.659    1.360
##    .rwa6              2.507    0.059   42.466    0.000    2.507    1.333
##     DISC              0.000                               0.000    0.000
##     SDO               0.000                               0.000    0.000
##     MINRWA            0.000                               0.000    0.000
##     GENRWA            0.000                               0.000    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NEG1              2.206    0.161   13.726    0.000    2.206    0.542
##    .NEG2              3.167    0.210   15.060    0.000    3.167    0.565
##    .NEG3              2.731    0.164   16.697    0.000    2.731    0.657
##    .NEG5              1.871    0.153   12.240    0.000    1.871    0.468
##    .NEGn              1.734    0.120   14.409    0.000    1.734    0.519
##    .SDOI              1.382    0.112   12.346    0.000    1.382    0.538
##    .SDOII             1.832    0.148   12.392    0.000    1.832    0.591
##    .SDOIII            2.418    0.140   17.317    0.000    2.418    0.670
##    .SDOIV             1.501    0.125   12.052    0.000    1.501    0.689
##    .rwa2              1.868    0.207    9.029    0.000    1.868    0.362
##    .rwa3              0.802    0.154    5.213    0.000    0.802    0.226
##    .rwa1              2.021    0.124   16.362    0.000    2.021    0.609
##    .rwa4              2.067    0.171   12.104    0.000    2.067    0.609
##    .rwa5              1.415    0.173    8.160    0.000    1.415    0.370
##    .rwa6              2.844    0.159   17.867    0.000    2.844    0.804
##     DISC              1.000                               1.000    1.000
##     SDO               1.000                               1.000    1.000
##     MINRWA            1.000                               1.000    1.000
##     GENRWA            1.000                               1.000    1.000
semPaths(FIT_MORAL_MODEL1, "Standardized", "Estimates")

Model Modification

modificationIndices(FIT_MORAL_MODEL1, sort.=TRUE, minimum.value=10)
##        lhs op    rhs     mi    epc sepc.lv sepc.all sepc.nox
## 105   NEG1 ~~   NEG2 83.969  1.008   1.008    0.382    0.382
## 144   NEG5 ~~   NEGn 64.304  0.685   0.685    0.380    0.380
## 69    DISC =~   rwa6 53.915  0.501   0.501    0.266    0.266
## 175  SDOII ~~  SDOIV 43.644  0.492   0.492    0.297    0.297
## 85  MINRWA =~   NEGn 36.730 -0.387  -0.387   -0.211   -0.211
## 182 SDOIII ~~  SDOIV 34.486 -0.458  -0.458   -0.240   -0.240
## 166   SDOI ~~ SDOIII 25.307  0.458   0.458    0.251    0.251
## 120   NEG2 ~~   NEG5 22.906 -0.525  -0.525   -0.216   -0.216
## 61    DISC =~  SDOII 22.515 -0.324  -0.324   -0.184   -0.184
## 93  MINRWA =~   rwa6 21.669  0.352   0.352    0.187    0.187
## 63    DISC =~  SDOIV 21.531  0.265   0.265    0.180    0.180
## 72     SDO =~   NEG3 20.315  0.348   0.348    0.171    0.171
## 68    DISC =~   rwa5 17.130 -0.293  -0.293   -0.150   -0.150
## 193  SDOIV ~~   rwa5 16.569 -0.253  -0.253   -0.174   -0.174
## 106   NEG1 ~~   NEG3 16.432 -0.388  -0.388   -0.158   -0.158
## 119   NEG2 ~~   NEG3 15.542 -0.445  -0.445   -0.151   -0.151
## 131   NEG2 ~~   rwa6 15.246  0.407   0.407    0.136    0.136
## 205   rwa1 ~~   rwa5 14.937  0.467   0.467    0.276    0.276
## 95  GENRWA =~   NEG2 14.273  0.297   0.297    0.126    0.126
## 98  GENRWA =~   NEGn 13.935 -0.223  -0.223   -0.122   -0.122
## 73     SDO =~   NEG5 13.595 -0.262  -0.262   -0.131   -0.131
## 83  MINRWA =~   NEG3 13.099  0.270   0.270    0.133    0.133
## 165   SDOI ~~  SDOII 13.003 -0.327  -0.327   -0.205   -0.205
## 107   NEG1 ~~   NEG5 12.262 -0.328  -0.328   -0.162   -0.162
## 126   NEG2 ~~   rwa2 11.886  0.334   0.334    0.137    0.137
## 67    DISC =~   rwa4 11.573  0.216   0.216    0.117    0.117
## 186 SDOIII ~~   rwa4 11.221 -0.272  -0.272   -0.122   -0.122
## 66    DISC =~   rwa1 11.118 -0.210  -0.210   -0.115   -0.115
## 80     SDO =~   rwa6 11.049  0.252   0.252    0.134    0.134
## 82  MINRWA =~   NEG2 10.933  0.277   0.277    0.117    0.117
## 164   NEGn ~~   rwa6 10.830  0.258   0.258    0.116    0.116
## 97  GENRWA =~   NEG5 10.820 -0.212  -0.212   -0.106   -0.106
## 159   NEGn ~~   rwa2 10.717 -0.239  -0.239   -0.133   -0.133
## 104 GENRWA =~   rwa3 10.298 -0.321  -0.321   -0.170   -0.170
## 103 GENRWA =~   rwa2 10.298  0.352   0.352    0.155    0.155
## 96  GENRWA =~   NEG3 10.159  0.224   0.224    0.110    0.110
## 116   NEG1 ~~   rwa4 10.131  0.250   0.250    0.117    0.117
## 148   NEG5 ~~  SDOIV 10.074  0.200   0.200    0.119    0.119

Modified Model

MORAL_MODEL1 <- " 
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6
NEG5 ~~ NEGn
NEG2 ~~ NEG1"
FIT_MORAL_MODEL1 <- cfa(MORAL_MODEL1, data=MOREX, std.lv=TRUE,missing="fiml", estimator="MLR")

Model Results and Plot

summary(FIT_MORAL_MODEL1, fit.measures=TRUE, standardized=TRUE)
## lavaan (0.6-1) converged normally after  36 iterations
## 
##   Number of observations                          1015
##   Number of missing patterns                         1
## 
##   Estimator                                         ML      Robust
##   Model Fit Test Statistic                     407.497     339.759
##   Degrees of freedom                                82          82
##   P-value (Chi-square)                           0.000       0.000
##   Scaling correction factor                                  1.199
##     for the Yuan-Bentler correction (Mplus variant)
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             4643.668    3640.092
##   Degrees of freedom                               105         105
##   P-value                                        0.000       0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.928       0.927
##   Tucker-Lewis Index (TLI)                       0.908       0.907
## 
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -29237.490  -29237.490
##   Loglikelihood unrestricted model (H1)     -29033.742  -29033.742
## 
##   Number of free parameters                         53          53
##   Akaike (AIC)                               58580.981   58580.981
##   Bayesian (BIC)                             58841.881   58841.881
##   Sample-size adjusted Bayesian (BIC)        58673.548   58673.548
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.063       0.056
##   90 Percent Confidence Interval          0.057  0.069       0.050  0.061
##   P-value RMSEA <= 0.05                          0.000       0.047
## 
##   Robust RMSEA                                                  NA
##   90 Percent Confidence Interval                                NA     NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.053       0.053
## 
## Parameter Estimates:
## 
##   Information                                 Observed
##   Observed information based on                Hessian
##   Standard Errors                   Robust.huber.white
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   DISC =~                                                               
##     NEG1              1.277    0.070   18.171    0.000    1.277    0.633
##     NEG2              1.461    0.078   18.839    0.000    1.461    0.617
##     NEG3              1.271    0.070   18.062    0.000    1.271    0.623
##     NEG5              1.393    0.065   21.279    0.000    1.393    0.697
##     NEGn              1.180    0.063   18.770    0.000    1.180    0.645
##   SDO =~                                                                
##     SDOI              1.089    0.058   18.645    0.000    1.089    0.679
##     SDOII             1.127    0.063   17.910    0.000    1.127    0.640
##     SDOIII            1.089    0.066   16.463    0.000    1.089    0.573
##     SDOIV             0.825    0.061   13.518    0.000    0.825    0.559
##   MINRWA =~                                                             
##     rwa2              1.810    0.063   28.946    0.000    1.810    0.797
##     rwa3              1.661    0.056   29.474    0.000    1.661    0.882
##   GENRWA =~                                                             
##     rwa1              1.140    0.062   18.531    0.000    1.140    0.626
##     rwa4              1.151    0.062   18.518    0.000    1.151    0.625
##     rwa5              1.553    0.063   24.744    0.000    1.553    0.794
##     rwa6              0.833    0.073   11.386    0.000    0.833    0.443
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .NEG5 ~~                                                               
##    .NEGn              0.506    0.120    4.206    0.000    0.506    0.253
##  .NEG1 ~~                                                               
##    .NEG2              0.840    0.149    5.619    0.000    0.840    0.289
##   DISC ~~                                                               
##     SDO               0.471    0.041   11.424    0.000    0.471    0.471
##     MINRWA            0.529    0.036   14.890    0.000    0.529    0.529
##     GENRWA            0.383    0.046    8.371    0.000    0.383    0.383
##   SDO ~~                                                                
##     MINRWA            0.426    0.039   10.846    0.000    0.426    0.426
##     GENRWA            0.420    0.042   10.080    0.000    0.420    0.420
##   MINRWA ~~                                                             
##     GENRWA            0.505    0.038   13.184    0.000    0.505    0.505
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NEG1              2.730    0.063   43.118    0.000    2.730    1.353
##    .NEG2              3.828    0.074   51.500    0.000    3.828    1.616
##    .NEG3              3.866    0.064   60.407    0.000    3.866    1.896
##    .NEG5              2.903    0.063   46.259    0.000    2.903    1.452
##    .NEGn              2.725    0.057   47.490    0.000    2.725    1.491
##    .SDOI              2.502    0.050   49.727    0.000    2.502    1.561
##    .SDOII             2.795    0.055   50.560    0.000    2.795    1.587
##    .SDOIII            3.838    0.060   64.377    0.000    3.838    2.021
##    .SDOIV             2.151    0.046   46.426    0.000    2.151    1.457
##    .rwa2              3.448    0.071   48.346    0.000    3.448    1.517
##    .rwa3              2.809    0.059   47.537    0.000    2.809    1.492
##    .rwa1              2.815    0.057   49.206    0.000    2.815    1.544
##    .rwa4              2.556    0.058   44.211    0.000    2.556    1.388
##    .rwa5              2.659    0.061   43.318    0.000    2.659    1.360
##    .rwa6              2.507    0.059   42.466    0.000    2.507    1.333
##     DISC              0.000                               0.000    0.000
##     SDO               0.000                               0.000    0.000
##     MINRWA            0.000                               0.000    0.000
##     GENRWA            0.000                               0.000    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NEG1              2.439    0.167   14.644    0.000    2.439    0.599
##    .NEG2              3.472    0.224   15.484    0.000    3.472    0.619
##    .NEG3              2.542    0.177   14.341    0.000    2.542    0.611
##    .NEG5              2.058    0.160   12.855    0.000    2.058    0.515
##    .NEGn              1.950    0.127   15.407    0.000    1.950    0.583
##    .SDOI              1.384    0.112   12.375    0.000    1.384    0.538
##    .SDOII             1.830    0.147   12.411    0.000    1.830    0.590
##    .SDOIII            2.423    0.139   17.383    0.000    2.423    0.671
##    .SDOIV             1.498    0.125   12.030    0.000    1.498    0.687
##    .rwa2              1.887    0.205    9.189    0.000    1.887    0.365
##    .rwa3              0.786    0.153    5.138    0.000    0.786    0.222
##    .rwa1              2.021    0.123   16.378    0.000    2.021    0.609
##    .rwa4              2.068    0.170   12.129    0.000    2.068    0.610
##    .rwa5              1.412    0.173    8.150    0.000    1.412    0.369
##    .rwa6              2.845    0.159   17.908    0.000    2.845    0.804
##     DISC              1.000                               1.000    1.000
##     SDO               1.000                               1.000    1.000
##     MINRWA            1.000                               1.000    1.000
##     GENRWA            1.000                               1.000    1.000
semPaths(FIT_MORAL_MODEL1, "Standardized", "Estimates")

Structural Model

The path coefficients from MINRWA to discrimination (\(\beta\) = .37, p < .001), was significantly positive. The path coefficients from SDO to discrimination (\(\beta\) = .28, p < .001), was also significantly positive. No significant effect was found between GENRWA and discrimination.

MORAL_MODEL1 <- " 
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6
DISC ~ MINRWA + GENRWA +  SDO
NEG2 ~~ NEG1
NEGn ~~ NEG5"
SEM_MORAL_MODEL1 <- sem(MORAL_MODEL1, data=MOREX, estimator = "MLR")

Model Results and the Plot

summary(SEM_MORAL_MODEL1, standardized=TRUE)
## lavaan (0.6-1) converged normally after  60 iterations
## 
##   Number of observations                          1015
## 
##   Estimator                                         ML      Robust
##   Model Fit Test Statistic                     407.497     339.759
##   Degrees of freedom                                82          82
##   P-value (Chi-square)                           0.000       0.000
##   Scaling correction factor                                  1.199
##     for the Yuan-Bentler correction (Mplus variant)
## 
## Parameter Estimates:
## 
##   Information                                 Observed
##   Observed information based on                Hessian
##   Standard Errors                   Robust.huber.white
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   DISC =~                                                               
##     NEG1              1.000                               1.277    0.633
##     NEG2              1.144    0.066   17.410    0.000    1.461    0.617
##     NEG3              0.996    0.085   11.685    0.000    1.271    0.623
##     NEG5              1.091    0.075   14.583    0.000    1.393    0.697
##     NEGn              0.924    0.065   14.320    0.000    1.180    0.645
##   SDO =~                                                                
##     SDOI              1.000                               1.089    0.679
##     SDOII             1.035    0.087   11.836    0.000    1.127    0.640
##     SDOIII            1.000    0.065   15.388    0.000    1.089    0.573
##     SDOIV             0.758    0.076    9.974    0.000    0.825    0.559
##   MINRWA =~                                                             
##     rwa2              1.000                               1.810    0.797
##     rwa3              0.917    0.048   18.950    0.000    1.661    0.882
##   GENRWA =~                                                             
##     rwa1              1.000                               1.140    0.626
##     rwa4              1.009    0.073   13.920    0.000    1.151    0.625
##     rwa5              1.362    0.079   17.287    0.000    1.553    0.794
##     rwa6              0.730    0.080    9.104    0.000    0.833    0.443
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   DISC ~                                                                
##     MINRWA            0.261    0.040    6.515    0.000    0.370    0.370
##     GENRWA            0.088    0.066    1.340    0.180    0.079    0.079
##     SDO               0.328    0.060    5.445    0.000    0.280    0.280
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .NEG1 ~~                                                               
##    .NEG2              0.840    0.149    5.619    0.000    0.840    0.289
##  .NEG5 ~~                                                               
##    .NEGn              0.506    0.120    4.206    0.000    0.506    0.253
##   SDO ~~                                                                
##     MINRWA            0.839    0.089    9.412    0.000    0.426    0.426
##     GENRWA            0.521    0.067    7.736    0.000    0.420    0.420
##   MINRWA ~~                                                             
##     GENRWA            1.043    0.111    9.371    0.000    0.505    0.505
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NEG1              2.439    0.167   14.644    0.000    2.439    0.599
##    .NEG2              3.472    0.224   15.484    0.000    3.472    0.619
##    .NEG3              2.542    0.177   14.342    0.000    2.542    0.611
##    .NEG5              2.058    0.160   12.855    0.000    2.058    0.515
##    .NEGn              1.950    0.127   15.407    0.000    1.950    0.583
##    .SDOI              1.384    0.112   12.375    0.000    1.384    0.538
##    .SDOII             1.830    0.147   12.411    0.000    1.830    0.590
##    .SDOIII            2.423    0.139   17.383    0.000    2.423    0.671
##    .SDOIV             1.498    0.125   12.030    0.000    1.498    0.687
##    .rwa2              1.887    0.205    9.189    0.000    1.887    0.365
##    .rwa3              0.786    0.153    5.138    0.000    0.786    0.222
##    .rwa1              2.021    0.123   16.378    0.000    2.021    0.609
##    .rwa4              2.068    0.170   12.129    0.000    2.068    0.610
##    .rwa5              1.412    0.173    8.150    0.000    1.412    0.369
##    .rwa6              2.845    0.159   17.908    0.000    2.845    0.804
##    .DISC              1.047    0.126    8.298    0.000    0.642    0.642
##     SDO               1.186    0.127    9.322    0.000    1.000    1.000
##     MINRWA            3.277    0.226   14.473    0.000    1.000    1.000
##     GENRWA            1.300    0.140    9.265    0.000    1.000    1.000
semPaths(SEM_MORAL_MODEL1, "Standardized", "Estimates")

Hypothesis II

Next, we hypothesized that moral exclusion will mediate the relationship between endorsement of the two forms of prejudice and negative intergroup attitudes against the Roma.

Confirmatory Factor Analysis

Confirmatory factor analysis was computed for the structural model. The errors of two items were allowed to covarry in order to improve the model fit. The model \(x^{2}\) of 533.378 showed no absolute fit (p < .001), while other measures confirmed that the model fit is overally satisfactory: CFI = .930; TLI = .912; SRMR = .054; and RMSEA = .062 and 90% CI = .057 - .068.

Model Specification

MORAL_MODEL2 <- "
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6
MEX =~ MEX2 + MEX3"
FIT_MORAL_MODEL2 <- cfa(MORAL_MODEL2, data=MOREX, std.lv=TRUE,missing="fiml", estimator="MLR")

Model Results and Plot

summary(FIT_MORAL_MODEL2, fit.measures=TRUE, standardized=TRUE)
## lavaan (0.6-1) converged normally after  30 iterations
## 
##   Number of observations                          1015
##   Number of missing patterns                         1
## 
##   Estimator                                         ML      Robust
##   Model Fit Test Statistic                     608.250     513.420
##   Degrees of freedom                               109         109
##   P-value (Chi-square)                           0.000       0.000
##   Scaling correction factor                                  1.185
##     for the Yuan-Bentler correction (Mplus variant)
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             6245.860    5015.428
##   Degrees of freedom                               136         136
##   P-value                                        0.000       0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.918       0.917
##   Tucker-Lewis Index (TLI)                       0.898       0.897
## 
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -32774.717  -32774.717
##   Loglikelihood unrestricted model (H1)     -32470.592  -32470.592
## 
##   Number of free parameters                         61          61
##   Akaike (AIC)                               65671.434   65671.434
##   Bayesian (BIC)                             65971.715   65971.715
##   Sample-size adjusted Bayesian (BIC)        65777.974   65777.974
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.067       0.060
##   90 Percent Confidence Interval          0.062  0.072       0.056  0.065
##   P-value RMSEA <= 0.05                          0.000       0.000
## 
##   Robust RMSEA                                                  NA
##   90 Percent Confidence Interval                                NA     NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.054       0.054
## 
## Parameter Estimates:
## 
##   Information                                 Observed
##   Observed information based on                Hessian
##   Standard Errors                   Robust.huber.white
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   DISC =~                                                               
##     NEG1              1.316    0.063   20.894    0.000    1.316    0.652
##     NEG2              1.584    0.062   25.446    0.000    1.584    0.669
##     NEG3              1.269    0.065   19.646    0.000    1.269    0.622
##     NEG5              1.434    0.056   25.637    0.000    1.434    0.717
##     NEGn              1.254    0.057   22.075    0.000    1.254    0.686
##   SDO =~                                                                
##     SDOI              1.090    0.058   18.695    0.000    1.090    0.680
##     SDOII             1.125    0.063   17.840    0.000    1.125    0.639
##     SDOIII            1.093    0.066   16.484    0.000    1.093    0.576
##     SDOIV             0.823    0.061   13.527    0.000    0.823    0.558
##   MINRWA =~                                                             
##     rwa2              1.812    0.062   29.251    0.000    1.812    0.798
##     rwa3              1.659    0.056   29.773    0.000    1.659    0.881
##   GENRWA =~                                                             
##     rwa1              1.141    0.062   18.548    0.000    1.141    0.626
##     rwa4              1.154    0.062   18.517    0.000    1.154    0.626
##     rwa5              1.548    0.063   24.516    0.000    1.548    0.792
##     rwa6              0.835    0.073   11.389    0.000    0.835    0.444
##   MEX =~                                                                
##     MEX2              1.842    0.040   45.762    0.000    1.842    0.916
##     MEX3              1.573    0.048   32.935    0.000    1.573    0.831
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   DISC ~~                                                               
##     SDO               0.446    0.040   11.296    0.000    0.446    0.446
##     MINRWA            0.504    0.034   14.764    0.000    0.504    0.504
##     GENRWA            0.367    0.044    8.428    0.000    0.367    0.367
##     MEX               0.806    0.019   41.574    0.000    0.806    0.806
##   SDO ~~                                                                
##     MINRWA            0.426    0.039   10.841    0.000    0.426    0.426
##     GENRWA            0.420    0.042   10.101    0.000    0.420    0.420
##     MEX               0.322    0.037    8.773    0.000    0.322    0.322
##   MINRWA ~~                                                             
##     GENRWA            0.506    0.038   13.255    0.000    0.506    0.506
##     MEX               0.381    0.033   11.562    0.000    0.381    0.381
##   GENRWA ~~                                                             
##     MEX               0.212    0.040    5.344    0.000    0.212    0.212
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NEG1              2.730    0.063   43.118    0.000    2.730    1.353
##    .NEG2              3.828    0.074   51.500    0.000    3.828    1.616
##    .NEG3              3.866    0.064   60.407    0.000    3.866    1.896
##    .NEG5              2.903    0.063   46.259    0.000    2.903    1.452
##    .NEGn              2.725    0.057   47.490    0.000    2.725    1.491
##    .SDOI              2.502    0.050   49.727    0.000    2.502    1.561
##    .SDOII             2.795    0.055   50.560    0.000    2.795    1.587
##    .SDOIII            3.838    0.060   64.377    0.000    3.838    2.021
##    .SDOIV             2.151    0.046   46.426    0.000    2.151    1.457
##    .rwa2              3.448    0.071   48.346    0.000    3.448    1.517
##    .rwa3              2.809    0.059   47.537    0.000    2.809    1.492
##    .rwa1              2.815    0.057   49.206    0.000    2.815    1.544
##    .rwa4              2.556    0.058   44.211    0.000    2.556    1.388
##    .rwa5              2.659    0.061   43.318    0.000    2.659    1.360
##    .rwa6              2.507    0.059   42.466    0.000    2.507    1.333
##    .MEX2              4.655    0.063   73.726    0.000    4.655    2.314
##    .MEX3              5.322    0.059   89.542    0.000    5.322    2.811
##     DISC              0.000                               0.000    0.000
##     SDO               0.000                               0.000    0.000
##     MINRWA            0.000                               0.000    0.000
##     GENRWA            0.000                               0.000    0.000
##     MEX               0.000                               0.000    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NEG1              2.337    0.144   16.275    0.000    2.337    0.574
##    .NEG2              3.097    0.187   16.598    0.000    3.097    0.552
##    .NEG3              2.547    0.161   15.870    0.000    2.547    0.613
##    .NEG5              1.943    0.126   15.407    0.000    1.943    0.486
##    .NEGn              1.770    0.107   16.556    0.000    1.770    0.530
##    .SDOI              1.382    0.111   12.427    0.000    1.382    0.538
##    .SDOII             1.835    0.148   12.431    0.000    1.835    0.592
##    .SDOIII            2.413    0.140   17.254    0.000    2.413    0.669
##    .SDOIV             1.502    0.124   12.064    0.000    1.502    0.689
##    .rwa2              1.879    0.204    9.205    0.000    1.879    0.364
##    .rwa3              0.793    0.150    5.274    0.000    0.793    0.224
##    .rwa1              2.019    0.124   16.345    0.000    2.019    0.608
##    .rwa4              2.061    0.171   12.066    0.000    2.061    0.608
##    .rwa5              1.428    0.174    8.202    0.000    1.428    0.373
##    .rwa6              2.841    0.159   17.831    0.000    2.841    0.803
##    .MEX2              0.654    0.096    6.821    0.000    0.654    0.162
##    .MEX3              1.110    0.089   12.492    0.000    1.110    0.310
##     DISC              1.000                               1.000    1.000
##     SDO               1.000                               1.000    1.000
##     MINRWA            1.000                               1.000    1.000
##     GENRWA            1.000                               1.000    1.000
##     MEX               1.000                               1.000    1.000
semPaths(FIT_MORAL_MODEL2, "Standardized", "Estimates")

Model Modification

modificationIndices(FIT_MORAL_MODEL2, sort.=TRUE, minimum.value=10)
##        lhs op    rhs     mi    epc sepc.lv sepc.all sepc.nox
## 140   NEG1 ~~   NEG2 77.767  0.894   0.894    0.332    0.332
## 185   NEG5 ~~   NEGn 60.807  0.585   0.585    0.315    0.315
## 81    DISC =~   rwa6 49.486  0.468   0.468    0.249    0.249
## 222  SDOII ~~  SDOIV 43.862  0.492   0.492    0.297    0.297
## 156   NEG2 ~~   NEG3 34.946 -0.615  -0.615   -0.219   -0.219
## 231 SDOIII ~~  SDOIV 34.939 -0.461  -0.461   -0.242   -0.242
## 101 MINRWA =~   NEGn 34.035 -0.369  -0.369   -0.202   -0.202
## 139    MEX =~   rwa6 27.748  0.318   0.318    0.169    0.169
## 211   SDOI ~~ SDOIII 24.725  0.453   0.453    0.248    0.248
## 73    DISC =~  SDOII 23.242 -0.322  -0.322   -0.183   -0.183
## 109 MINRWA =~   rwa6 21.372  0.349   0.349    0.185    0.185
## 141   NEG1 ~~   NEG3 19.685 -0.397  -0.397   -0.163   -0.163
## 127    MEX =~   NEG3 19.665  0.563   0.563    0.276    0.276
## 75    DISC =~  SDOIV 19.645  0.247   0.247    0.168    0.168
## 170   NEG2 ~~   MEX3 18.554  0.319   0.319    0.172    0.172
## 244  SDOIV ~~   rwa5 16.659 -0.254  -0.254   -0.173   -0.173
## 157   NEG2 ~~   NEG5 16.591 -0.398  -0.398   -0.162   -0.162
## 131    MEX =~  SDOII 15.665 -0.233  -0.233   -0.132   -0.132
## 209   NEGn ~~   MEX3 15.641 -0.224  -0.224   -0.160   -0.160
## 262   rwa1 ~~   rwa5 15.533  0.472   0.472    0.278    0.278
## 123 GENRWA =~   MEX2 15.507 -0.220  -0.220   -0.110   -0.110
## 124 GENRWA =~   MEX3 15.507  0.188   0.188    0.099    0.099
## 168   NEG2 ~~   rwa6 15.182  0.397   0.397    0.134    0.134
## 86     SDO =~   NEG3 14.060  0.283   0.283    0.139    0.139
## 125    MEX =~   NEG1 14.034 -0.467  -0.467   -0.231   -0.231
## 83    DISC =~   MEX3 13.555  1.495   1.495    0.789    0.789
## 82    DISC =~   MEX2 13.553 -1.750  -1.750   -0.870   -0.870
## 111 MINRWA =~   MEX3 13.539  0.189   0.189    0.100    0.100
## 110 MINRWA =~   MEX2 13.539 -0.221  -0.221   -0.110   -0.110
## 116 GENRWA =~   NEGn 13.086 -0.215  -0.215   -0.118   -0.118
## 197   NEG5 ~~   MEX3 12.605 -0.216  -0.216   -0.147   -0.147
## 210   SDOI ~~  SDOII 12.432 -0.319  -0.319   -0.200   -0.200
## 80    DISC =~   rwa5 12.287 -0.239  -0.239   -0.122   -0.122
## 163   NEG2 ~~   rwa2 11.969  0.327   0.327    0.136    0.136
## 113 GENRWA =~   NEG2 11.634  0.265   0.265    0.112    0.112
## 87     SDO =~   NEG5 11.600 -0.240  -0.240   -0.120   -0.120
## 78    DISC =~   rwa1 11.523 -0.208  -0.208   -0.114   -0.114
## 133    MEX =~  SDOIV 11.037  0.164   0.164    0.111    0.111
## 207   NEGn ~~   rwa6 10.978  0.257   0.257    0.115    0.115
## 235 SDOIII ~~   rwa4 10.973 -0.269  -0.269   -0.121   -0.121
## 94     SDO =~   rwa6 10.944  0.251   0.251    0.133    0.133
## 151   NEG1 ~~   rwa4 10.872  0.260   0.260    0.118    0.118
## 121 GENRWA =~   rwa2 10.487  0.346   0.346    0.152    0.152
## 122 GENRWA =~   rwa3 10.487 -0.317  -0.317   -0.168   -0.168
## 236 SDOIII ~~   rwa5 10.205  0.254   0.254    0.137    0.137
## 189   NEG5 ~~  SDOIV 10.051  0.198   0.198    0.116    0.116

Modified Model

MORAL_MODEL2 <- "
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6
MEX =~ MEX2 + MEX3
NEG2 ~~ NEG1"
FIT_MORAL_MODEL2 <- cfa(MORAL_MODEL2, data=MOREX, std.lv=TRUE,missing="fiml", estimator="MLR")

Model Results and Plot

summary(FIT_MORAL_MODEL2, fit.measures=TRUE, standardized=TRUE)
## lavaan (0.6-1) converged normally after  33 iterations
## 
##   Number of observations                          1015
##   Number of missing patterns                         1
## 
##   Estimator                                         ML      Robust
##   Model Fit Test Statistic                     533.378     450.842
##   Degrees of freedom                               108         108
##   P-value (Chi-square)                           0.000       0.000
##   Scaling correction factor                                  1.183
##     for the Yuan-Bentler correction (Mplus variant)
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             6245.860    5015.428
##   Degrees of freedom                               136         136
##   P-value                                        0.000       0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.930       0.930
##   Tucker-Lewis Index (TLI)                       0.912       0.912
## 
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -32737.281  -32737.281
##   Loglikelihood unrestricted model (H1)     -32470.592  -32470.592
## 
##   Number of free parameters                         62          62
##   Akaike (AIC)                               65598.562   65598.562
##   Bayesian (BIC)                             65903.766   65903.766
##   Sample-size adjusted Bayesian (BIC)        65706.849   65706.849
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.062       0.056
##   90 Percent Confidence Interval          0.057  0.068       0.051  0.061
##   P-value RMSEA <= 0.05                          0.000       0.023
## 
##   Robust RMSEA                                                  NA
##   90 Percent Confidence Interval                                NA     NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.054       0.054
## 
## Parameter Estimates:
## 
##   Information                                 Observed
##   Observed information based on                Hessian
##   Standard Errors                   Robust.huber.white
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   DISC =~                                                               
##     NEG1              1.227    0.063   19.488    0.000    1.227    0.608
##     NEG2              1.481    0.066   22.367    0.000    1.481    0.625
##     NEG3              1.305    0.063   20.573    0.000    1.305    0.640
##     NEG5              1.457    0.057   25.397    0.000    1.457    0.729
##     NEGn              1.272    0.059   21.507    0.000    1.272    0.696
##   SDO =~                                                                
##     SDOI              1.087    0.058   18.678    0.000    1.087    0.678
##     SDOII             1.127    0.063   17.924    0.000    1.127    0.640
##     SDOIII            1.092    0.067   16.357    0.000    1.092    0.575
##     SDOIV             0.826    0.061   13.519    0.000    0.826    0.559
##   MINRWA =~                                                             
##     rwa2              1.806    0.062   28.996    0.000    1.806    0.795
##     rwa3              1.664    0.056   29.669    0.000    1.664    0.884
##   GENRWA =~                                                             
##     rwa1              1.142    0.061   18.577    0.000    1.142    0.626
##     rwa4              1.151    0.062   18.498    0.000    1.151    0.625
##     rwa5              1.552    0.063   24.715    0.000    1.552    0.794
##     rwa6              0.832    0.073   11.367    0.000    0.832    0.442
##   MEX =~                                                                
##     MEX2              1.852    0.040   45.959    0.000    1.852    0.921
##     MEX3              1.565    0.048   32.509    0.000    1.565    0.826
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .NEG1 ~~                                                               
##    .NEG2              0.888    0.125    7.094    0.000    0.888    0.300
##   DISC ~~                                                               
##     SDO               0.445    0.041   10.871    0.000    0.445    0.445
##     MINRWA            0.496    0.035   13.982    0.000    0.496    0.496
##     GENRWA            0.355    0.045    7.970    0.000    0.355    0.355
##     MEX               0.812    0.021   39.059    0.000    0.812    0.812
##   SDO ~~                                                                
##     MINRWA            0.426    0.039   10.859    0.000    0.426    0.426
##     GENRWA            0.420    0.042   10.081    0.000    0.420    0.420
##     MEX               0.321    0.037    8.719    0.000    0.321    0.321
##   MINRWA ~~                                                             
##     GENRWA            0.505    0.038   13.123    0.000    0.505    0.505
##     MEX               0.379    0.033   11.444    0.000    0.379    0.379
##   GENRWA ~~                                                             
##     MEX               0.209    0.040    5.265    0.000    0.209    0.209
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NEG1              2.730    0.063   43.118    0.000    2.730    1.353
##    .NEG2              3.828    0.074   51.500    0.000    3.828    1.616
##    .NEG3              3.866    0.064   60.407    0.000    3.866    1.896
##    .NEG5              2.903    0.063   46.259    0.000    2.903    1.452
##    .NEGn              2.725    0.057   47.490    0.000    2.725    1.491
##    .SDOI              2.502    0.050   49.727    0.000    2.502    1.561
##    .SDOII             2.795    0.055   50.560    0.000    2.795    1.587
##    .SDOIII            3.838    0.060   64.377    0.000    3.838    2.021
##    .SDOIV             2.151    0.046   46.426    0.000    2.151    1.457
##    .rwa2              3.448    0.071   48.346    0.000    3.448    1.517
##    .rwa3              2.809    0.059   47.537    0.000    2.809    1.492
##    .rwa1              2.815    0.057   49.206    0.000    2.815    1.544
##    .rwa4              2.556    0.058   44.211    0.000    2.556    1.388
##    .rwa5              2.659    0.061   43.318    0.000    2.659    1.360
##    .rwa6              2.507    0.059   42.466    0.000    2.507    1.333
##    .MEX2              4.655    0.063   73.726    0.000    4.655    2.314
##    .MEX3              5.322    0.059   89.542    0.000    5.322    2.811
##     DISC              0.000                               0.000    0.000
##     SDO               0.000                               0.000    0.000
##     MINRWA            0.000                               0.000    0.000
##     GENRWA            0.000                               0.000    0.000
##     MEX               0.000                               0.000    0.000
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .NEG1              2.564    0.146   17.500    0.000    2.564    0.630
##    .NEG2              3.413    0.190   17.970    0.000    3.413    0.609
##    .NEG3              2.455    0.160   15.326    0.000    2.455    0.591
##    .NEG5              1.875    0.131   14.356    0.000    1.875    0.469
##    .NEGn              1.724    0.113   15.233    0.000    1.724    0.516
##    .SDOI              1.388    0.111   12.484    0.000    1.388    0.540
##    .SDOII             1.831    0.147   12.428    0.000    1.831    0.590
##    .SDOIII            2.416    0.141   17.194    0.000    2.416    0.670
##    .SDOIV             1.498    0.125   12.028    0.000    1.498    0.687
##    .rwa2              1.902    0.205    9.274    0.000    1.902    0.368
##    .rwa3              0.774    0.152    5.080    0.000    0.774    0.218
##    .rwa1              2.018    0.123   16.349    0.000    2.018    0.608
##    .rwa4              2.068    0.171   12.123    0.000    2.068    0.610
##    .rwa5              1.415    0.174    8.147    0.000    1.415    0.370
##    .rwa6              2.847    0.159   17.901    0.000    2.847    0.804
##    .MEX2              0.615    0.097    6.308    0.000    0.615    0.152
##    .MEX3              1.138    0.090   12.636    0.000    1.138    0.317
##     DISC              1.000                               1.000    1.000
##     SDO               1.000                               1.000    1.000
##     MINRWA            1.000                               1.000    1.000
##     GENRWA            1.000                               1.000    1.000
##     MEX               1.000                               1.000    1.000
semPaths(FIT_MORAL_MODEL2, "Standardized", "Estimates")

Structural Model

To investigate whether moral exclusion mediates the relation between SDO, MINRWA, as well as GENRWA and discrimination a path model was tested. using bootstrapped standard errors of 5000, the results indicated that, there is a significant indirect effect between SDO and discrimination through moral exclusion, \(\beta\) = .143 , SE = .032, p < .001, 95% CI[.079, .205]. The path model also showed a significant direct effect, \(\beta\) = .126, SE = .042, p = .002, 95% CI[.086, .408]. Hence a partial mediation was found.

A bootstrap estimation approach with 5000 samples was used which indicated that both direct \(\beta\) = .143 , SE = .032, p < .001, 95% CI[.079, .205] and indirect \(\beta\) = .217 , SE = .032, p < .001, 95% CI[.156, .282] effects were also significant for the relationship between MINRWA and discrimination through the mediating effect of moral exclusion. Thus a partial mediation was again detected.

Although GENRWA significantly predicted discrimination \(\beta\) = .089, p = .044, no significant mediational effect was found between GENRWA and discriminatory attitudes through moral exclusion.

MORAL_MODEL2 <- " 
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6
MEX =~ MEX2 + MEX3
NEG2 ~~ NEG1
DISC ~ c1*SDO + c2*MINRWA + c3*GENRWA
MEX ~ a1*SDO + a2*MINRWA + a3*GENRWA
DISC ~ b*MEX
ab1 := a1*b
ab2 := a2*b
ab3 := a3*b
total1 := c1 + (a1 * b) 
total2 := c2 + (a2 * b)
total3 := c3 + (a3 * b)"
SEM_MORAL_MODEL2 <- sem(MORAL_MODEL2, data=MOREX, se = "bootstrap", bootstrap = 5000)

Model Results and the Plot

summary(SEM_MORAL_MODEL2, standardized=TRUE, ci = TRUE)
## lavaan (0.6-1) converged normally after  68 iterations
## 
##   Number of observations                          1015
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     533.378
##   Degrees of freedom                               108
##   P-value (Chi-square)                           0.000
## 
## Parameter Estimates:
## 
##   Information                                 Observed
##   Observed information based on                Hessian
##   Standard Errors                            Bootstrap
##   Number of requested bootstrap draws             5000
##   Number of successful bootstrap draws            5000
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   DISC =~                                                               
##     NEG1              1.000                               1.000    1.000
##     NEG2              1.207    0.064   18.742    0.000    1.094    1.345
##     NEG3              1.063    0.077   13.777    0.000    0.926    1.229
##     NEG5              1.188    0.070   17.022    0.000    1.064    1.338
##     NEGn              1.037    0.065   16.012    0.000    0.923    1.175
##   SDO =~                                                                
##     SDOI              1.000                               1.000    1.000
##     SDOII             1.037    0.091   11.441    0.000    0.876    1.232
##     SDOIII            1.004    0.066   15.159    0.000    0.886    1.146
##     SDOIV             0.760    0.079    9.615    0.000    0.617    0.927
##   MINRWA =~                                                             
##     rwa2              1.000                               1.000    1.000
##     rwa3              0.922    0.049   18.777    0.000    0.833    1.024
##   GENRWA =~                                                             
##     rwa1              1.000                               1.000    1.000
##     rwa4              1.008    0.074   13.571    0.000    0.870    1.162
##     rwa5              1.360    0.081   16.777    0.000    1.220    1.537
##     rwa6              0.729    0.081    9.045    0.000    0.581    0.898
##   MEX =~                                                                
##     MEX2              1.000                               1.000    1.000
##     MEX3              0.845    0.026   32.309    0.000    0.794    0.896
##    Std.lv  Std.all
##                   
##     1.227    0.608
##     1.481    0.625
##     1.305    0.640
##     1.457    0.729
##     1.272    0.696
##                   
##     1.087    0.678
##     1.127    0.640
##     1.092    0.575
##     0.826    0.559
##                   
##     1.806    0.795
##     1.664    0.884
##                   
##     1.142    0.626
##     1.151    0.625
##     1.552    0.794
##     0.832    0.442
##                   
##     1.852    0.921
##     1.565    0.826
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   DISC ~                                                                
##     SDO       (c1)    0.143    0.048    2.971    0.003    0.052    0.240
##     MINRWA    (c2)    0.089    0.031    2.840    0.005    0.026    0.149
##     GENRWA    (c3)    0.096    0.049    1.950    0.051    0.005    0.197
##   MEX ~                                                                 
##     SDO       (a1)    0.346    0.078    4.457    0.000    0.193    0.498
##     MINRWA    (a2)    0.316    0.046    6.942    0.000    0.228    0.406
##     GENRWA    (a3)   -0.052    0.082   -0.633    0.527   -0.216    0.107
##   DISC ~                                                                
##     MEX        (b)    0.465    0.028   16.414    0.000    0.410    0.520
##    Std.lv  Std.all
##                   
##     0.126    0.126
##     0.131    0.131
##     0.089    0.089
##                   
##     0.203    0.203
##     0.308    0.308
##    -0.032   -0.032
##                   
##     0.703    0.703
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##  .NEG1 ~~                                                               
##    .NEG2              0.888    0.124    7.141    0.000    0.650    1.137
##   SDO ~~                                                                
##     MINRWA            0.836    0.089    9.434    0.000    0.659    1.010
##     GENRWA            0.521    0.068    7.710    0.000    0.389    0.654
##   MINRWA ~~                                                             
##     GENRWA            1.040    0.112    9.310    0.000    0.829    1.266
##    Std.lv  Std.all
##                   
##     0.888    0.300
##                   
##     0.426    0.426
##     0.420    0.420
##                   
##     0.505    0.505
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .NEG1              2.564    0.146   17.516    0.000    2.276    2.847
##    .NEG2              3.413    0.188   18.166    0.000    3.040    3.779
##    .NEG3              2.455    0.159   15.448    0.000    2.140    2.762
##    .NEG5              1.875    0.131   14.311    0.000    1.617    2.124
##    .NEGn              1.724    0.114   15.128    0.000    1.496    1.944
##    .SDOI              1.388    0.111   12.483    0.000    1.184    1.609
##    .SDOII             1.831    0.148   12.332    0.000    1.534    2.121
##    .SDOIII            2.416    0.142   16.958    0.000    2.125    2.692
##    .SDOIV             1.498    0.124   12.033    0.000    1.252    1.744
##    .rwa2              1.902    0.205    9.294    0.000    1.504    2.301
##    .rwa3              0.774    0.155    4.985    0.000    0.461    1.075
##    .rwa1              2.018    0.124   16.231    0.000    1.764    2.256
##    .rwa4              2.068    0.170   12.159    0.000    1.735    2.399
##    .rwa5              1.415    0.175    8.089    0.000    1.077    1.758
##    .rwa6              2.847    0.159   17.875    0.000    2.525    3.151
##    .MEX2              0.615    0.099    6.222    0.000    0.419    0.806
##    .MEX3              1.138    0.091   12.471    0.000    0.964    1.321
##    .DISC              0.416    0.067    6.229    0.000    0.285    0.548
##     SDO               1.182    0.128    9.199    0.000    0.931    1.437
##     MINRWA            3.262    0.227   14.379    0.000    2.820    3.711
##     GENRWA            1.303    0.140    9.304    0.000    1.039    1.582
##    .MEX               2.831    0.159   17.762    0.000    2.507    3.135
##    Std.lv  Std.all
##     2.564    0.630
##     3.413    0.609
##     2.455    0.591
##     1.875    0.469
##     1.724    0.516
##     1.388    0.540
##     1.831    0.590
##     2.416    0.670
##     1.498    0.687
##     1.902    0.368
##     0.774    0.218
##     2.018    0.608
##     2.068    0.610
##     1.415    0.370
##     2.847    0.804
##     0.615    0.152
##     1.138    0.317
##     0.277    0.277
##     1.000    1.000
##     1.000    1.000
##     1.000    1.000
##     0.825    0.825
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ab1               0.161    0.037    4.372    0.000    0.088    0.235
##     ab2               0.147    0.023    6.396    0.000    0.102    0.194
##     ab3              -0.024    0.038   -0.634    0.526   -0.100    0.050
##     total1            0.304    0.059    5.164    0.000    0.192    0.420
##     total2            0.236    0.038    6.212    0.000    0.161    0.311
##     total3            0.072    0.063    1.134    0.257   -0.047    0.204
##    Std.lv  Std.all
##     0.143    0.143
##     0.217    0.217
##    -0.022   -0.022
##     0.269    0.269
##     0.348    0.348
##     0.067    0.067
parameterEstimates(SEM_MORAL_MODEL2, ci = TRUE, boot.ci.type = "norm", level = 0.95, standardized = TRUE)
##       lhs op       rhs  label    est    se      z pvalue ci.lower ci.upper
## 1    DISC =~      NEG1         1.000 0.000     NA     NA    1.000    1.000
## 2    DISC =~      NEG2         1.207 0.064 18.742  0.000    1.078    1.330
## 3    DISC =~      NEG3         1.063 0.077 13.777  0.000    0.908    1.210
## 4    DISC =~      NEG5         1.188 0.070 17.022  0.000    1.047    1.321
## 5    DISC =~      NEGn         1.037 0.065 16.012  0.000    0.907    1.160
## 6     SDO =~      SDOI         1.000 0.000     NA     NA    1.000    1.000
## 7     SDO =~     SDOII         1.037 0.091 11.441  0.000    0.852    1.208
## 8     SDO =~    SDOIII         1.004 0.066 15.159  0.000    0.871    1.131
## 9     SDO =~     SDOIV         0.760 0.079  9.615  0.000    0.599    0.909
## 10 MINRWA =~      rwa2         1.000 0.000     NA     NA    1.000    1.000
## 11 MINRWA =~      rwa3         0.922 0.049 18.777  0.000    0.823    1.015
## 12 GENRWA =~      rwa1         1.000 0.000     NA     NA    1.000    1.000
## 13 GENRWA =~      rwa4         1.008 0.074 13.571  0.000    0.860    1.151
## 14 GENRWA =~      rwa5         1.360 0.081 16.777  0.000    1.196    1.514
## 15 GENRWA =~      rwa6         0.729 0.081  9.045  0.000    0.567    0.883
## 16    MEX =~      MEX2         1.000 0.000     NA     NA    1.000    1.000
## 17    MEX =~      MEX3         0.845 0.026 32.309  0.000    0.794    0.896
## 18   NEG1 ~~      NEG2         0.888 0.124  7.141  0.000    0.646    1.133
## 19   DISC  ~       SDO     c1  0.143 0.048  2.971  0.003    0.048    0.236
## 20   DISC  ~    MINRWA     c2  0.089 0.031  2.840  0.005    0.029    0.152
## 21   DISC  ~    GENRWA     c3  0.096 0.049  1.950  0.051   -0.003    0.190
## 22    MEX  ~       SDO     a1  0.346 0.078  4.457  0.000    0.193    0.498
## 23    MEX  ~    MINRWA     a2  0.316 0.046  6.942  0.000    0.227    0.406
## 24    MEX  ~    GENRWA     a3 -0.052 0.082 -0.633  0.527   -0.212    0.110
## 25   DISC  ~       MEX      b  0.465 0.028 16.414  0.000    0.411    0.522
## 26   NEG1 ~~      NEG1         2.564 0.146 17.516  0.000    2.283    2.857
## 27   NEG2 ~~      NEG2         3.413 0.188 18.166  0.000    3.048    3.785
## 28   NEG3 ~~      NEG3         2.455 0.159 15.448  0.000    2.150    2.773
## 29   NEG5 ~~      NEG5         1.875 0.131 14.311  0.000    1.624    2.138
## 30   NEGn ~~      NEGn         1.724 0.114 15.128  0.000    1.507    1.954
## 31   SDOI ~~      SDOI         1.388 0.111 12.483  0.000    1.171    1.607
## 32  SDOII ~~     SDOII         1.831 0.148 12.332  0.000    1.547    2.129
## 33 SDOIII ~~    SDOIII         2.416 0.142 16.958  0.000    2.142    2.701
## 34  SDOIV ~~     SDOIV         1.498 0.124 12.033  0.000    1.258    1.746
## 35   rwa2 ~~      rwa2         1.902 0.205  9.294  0.000    1.504    2.306
## 36   rwa3 ~~      rwa3         0.774 0.155  4.985  0.000    0.478    1.086
## 37   rwa1 ~~      rwa1         2.018 0.124 16.231  0.000    1.779    2.266
## 38   rwa4 ~~      rwa4         2.068 0.170 12.159  0.000    1.738    2.404
## 39   rwa5 ~~      rwa5         1.415 0.175  8.089  0.000    1.080    1.765
## 40   rwa6 ~~      rwa6         2.847 0.159 17.875  0.000    2.545    3.169
## 41   MEX2 ~~      MEX2         0.615 0.099  6.222  0.000    0.425    0.812
## 42   MEX3 ~~      MEX3         1.138 0.091 12.471  0.000    0.959    1.317
## 43   DISC ~~      DISC         0.416 0.067  6.229  0.000    0.291    0.553
## 44    SDO ~~       SDO         1.182 0.128  9.199  0.000    0.932    1.436
## 45 MINRWA ~~    MINRWA         3.262 0.227 14.379  0.000    2.820    3.710
## 46 GENRWA ~~    GENRWA         1.303 0.140  9.304  0.000    1.029    1.578
## 47    MEX ~~       MEX         2.831 0.159 17.762  0.000    2.529    3.154
## 48    SDO ~~    MINRWA         0.836 0.089  9.434  0.000    0.667    1.015
## 49    SDO ~~    GENRWA         0.521 0.068  7.710  0.000    0.391    0.656
## 50 MINRWA ~~    GENRWA         1.040 0.112  9.310  0.000    0.821    1.259
## 51    ab1 :=      a1*b    ab1  0.161 0.037  4.372  0.000    0.089    0.233
## 52    ab2 :=      a2*b    ab2  0.147 0.023  6.396  0.000    0.102    0.193
## 53    ab3 :=      a3*b    ab3 -0.024 0.038 -0.634  0.526   -0.099    0.051
## 54 total1 := c1+(a1*b) total1  0.304 0.059  5.164  0.000    0.188    0.418
## 55 total2 := c2+(a2*b) total2  0.236 0.038  6.212  0.000    0.163    0.312
## 56 total3 := c3+(a3*b) total3  0.072 0.063  1.134  0.257   -0.055    0.194
##    std.lv std.all std.nox
## 1   1.227   0.608   0.608
## 2   1.481   0.625   0.625
## 3   1.305   0.640   0.640
## 4   1.457   0.729   0.729
## 5   1.272   0.696   0.696
## 6   1.087   0.678   0.678
## 7   1.127   0.640   0.640
## 8   1.092   0.575   0.575
## 9   0.826   0.559   0.559
## 10  1.806   0.795   0.795
## 11  1.664   0.884   0.884
## 12  1.142   0.626   0.626
## 13  1.151   0.625   0.625
## 14  1.552   0.794   0.794
## 15  0.832   0.442   0.442
## 16  1.852   0.921   0.921
## 17  1.565   0.826   0.826
## 18  0.888   0.300   0.300
## 19  0.126   0.126   0.126
## 20  0.131   0.131   0.131
## 21  0.089   0.089   0.089
## 22  0.203   0.203   0.203
## 23  0.308   0.308   0.308
## 24 -0.032  -0.032  -0.032
## 25  0.703   0.703   0.703
## 26  2.564   0.630   0.630
## 27  3.413   0.609   0.609
## 28  2.455   0.591   0.591
## 29  1.875   0.469   0.469
## 30  1.724   0.516   0.516
## 31  1.388   0.540   0.540
## 32  1.831   0.590   0.590
## 33  2.416   0.670   0.670
## 34  1.498   0.687   0.687
## 35  1.902   0.368   0.368
## 36  0.774   0.218   0.218
## 37  2.018   0.608   0.608
## 38  2.068   0.610   0.610
## 39  1.415   0.370   0.370
## 40  2.847   0.804   0.804
## 41  0.615   0.152   0.152
## 42  1.138   0.317   0.317
## 43  0.277   0.277   0.277
## 44  1.000   1.000   1.000
## 45  1.000   1.000   1.000
## 46  1.000   1.000   1.000
## 47  0.825   0.825   0.825
## 48  0.426   0.426   0.426
## 49  0.420   0.420   0.420
## 50  0.505   0.505   0.505
## 51  0.143   0.143   0.143
## 52  0.217   0.217   0.217
## 53 -0.022  -0.022  -0.022
## 54  0.269   0.269   0.269
## 55  0.348   0.348   0.348
## 56  0.067   0.067   0.067
semPaths(SEM_MORAL_MODEL2, "Standardized", "Estimates")

Conclusion

Taken together, the first model showed that social dominance orientation as well as those items of the RWA scale addressing minority issues significantly predict discriminatory attitudes against the Roma community in Hungary. Such effect was supported in the second model as well, which also showed that moral exclusion partially explains the mechanism.